Multi-Source, Multi-Dimensional, Cross-Entity, Multimedia Encryptmatics Database Platform Apparatuses, Methods and Systems

ABSTRACT

The MULTI-SOURCE, MULTI-DIMENSIONAL, CROSS-ENTITY, MULTIMEDIA ENCRYPTMATICS DATABASE PLATFORM APPARATUSES, METHODS AND SYSTEMS (“EXDB”) transform data aggregated from various computer resources using EXDB components into updated entity profiles and/or social graphs. In one implementation, the EXDB aggregates data records including search results, purchase transaction data, service usage data, service enrollment data, and social data. The EXDB identifies data field types within the data records and their associated data values. From the data field types and their associated data values, the EXDB identifies an entity. The EXDB generates correlations of the entity to other entities identifiable from the data field types and their associated data values. The EXDB also associates attributes to the entity by drawing inferences related to the entity from the data field types and their associated data values. Using the generated correlations and associated attributes, the EXDB generates an updated profile and social graph of the entity. The EXDB provides the updated profile and social graph for an automated web form filling request.

This application for letters patent disclosure document describesinventive aspects that include various novel innovations (hereinafter“disclosure”) and contains material that is subject to copyright, maskwork, and/or other intellectual property protection. The respectiveowners of such intellectual property have no objection to the facsimilereproduction of the disclosure by anyone as it appears in publishedPatent Office file/records, but otherwise reserve all rights.

PRIORITY CLAIM

This application is a non-provisional of and claims priority under 35USC §§119, 120 to: U.S. provisional patent application Ser. No.61/594,063 filed Feb. 2, 2012, entitled “CENTRALIZED PERSONALINFORMATION PLATFORM APPARATUSES, METHODS AND SYSTEMS,” attorney docketno. P-42185PRV|VISA-122/01US, and U.S. patent application Ser. No.13/520,481 filed Jul. 3, 2012, entitled “Universal Electronic PaymentApparatuses, Methods and Systems,” attorney docket no.P-42051US02|20270-136US.

This application hereby claims priority under 35 U.S.C. §365, 371 to PCTapplication no. PCT/US13/24538 filed Feb. 2, 2013, entitled“MULTI-SOURCE, MULTI-DIMENSIONAL, CROSS-ENTITY, MULTIMEDIA DATABASEPLATFORM APPARATUSES, METHODS AND SYSTEMS,” attorney docket no.P-42185WO01|VISA-122/01WO.

The entire contents of the aforementioned application(s) are expresslyincorporated by reference herein.

FIELD

The present innovations generally address apparatuses, methods andsystems for consumer data management and analytics, and moreparticularly, include MULTI-SOURCE, MULTI-DIMENSIONAL, CROSS-ENTITY,MULTIMEDIA ENCRYPTMATICS DATABASE PLATFORM APPARATUSES, METHODS ANDSYSTEMS (“EXDB”).

However, in order to develop a reader's understanding of theinnovations, disclosures have been compiled into a single description toillustrate and clarify how aspects of these innovations operateindependently, interoperate as between individual innovations, and/orcooperate collectively. The application goes on to further describe theinterrelations and synergies as between the various innovations; all ofwhich is to further compliance with 35 U.S.C. §112.

BACKGROUND

Many people share information through online social media streams usingvarious communication devices or applications. Moreover, consumers mayaccess online stores using the Internet on a computer or mobile deviceto make purchases from various service providers or merchants.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying appendices and/or drawings illustrate variousnon-limiting, example, innovative aspects in accordance with the presentdescriptions:

FIG. 1 shows a block diagram illustrating example aspects of acentralized personal information platform in some embodiments of theEXDB;

FIGS. 2A-F show block diagrams illustrating example aspects of datamodels within a centralized personal information platform in someembodiments of the EXDB;

FIG. 3 shows a block diagram illustrating example EXDB componentconfigurations in some embodiments of the EXDB;

FIG. 4 shows a data flow diagram illustrating an example search resultaggregation procedure in some embodiments of the EXDB;

FIG. 5 shows a logic flow diagram illustrating example aspects ofaggregating search results in some embodiments of the EXDB, e.g., aSearch Results Aggregation (“SRA”) component 500;

FIGS. 6A-D show data flow diagrams illustrating an example card-basedtransaction execution procedure in some embodiments of the EXDB;

FIGS. 7A-E show logic flow diagrams illustrating example aspects ofcard-based transaction execution, resulting in generation of card-basedtransaction data and service usage data, in some embodiments of theEXDB, e.g., a Card-Based Transaction Execution (“CTE”) component 700;

FIG. 8 shows a data flow diagram illustrating an example procedure toaggregate card-based transaction data in some embodiments of the EXDB;

FIG. 9 shows a logic flow diagram illustrating example aspects ofaggregating card-based transaction data in some embodiments of the EXDB,e.g., a Transaction Data Aggregation (“TDA”) component 900;

FIG. 10 shows a data flow diagram illustrating an example social dataaggregation procedure in some embodiments of the EXDB;

FIG. 11 shows a logic flow diagram illustrating example aspects ofaggregating social data in some embodiments of the EXDB, e.g., a SocialData Aggregation (“SDA”) component 1100;

FIG. 12 shows a data flow diagram illustrating an example procedure forenrollment in value-add services in some embodiments of the EXDB;

FIG. 13 shows a logic flow diagram illustrating example aspects ofsocial network payment authentication enrollment in some embodiments ofthe EXDB, e.g., a Value-Add Service Enrollment (“VASE”) component 1300;

FIGS. 14A-B show flow diagrams illustrating example aspects ofnormalizing aggregated search, enrolled, service usage, transactionand/or other aggregated data into a standardized data format in someembodiments of the EXDB, e.g., a Aggregated Data Record Normalization(“ADRN”) component 1400;

FIG. 15 shows a logic flow diagram illustrating example aspects ofrecognizing data fields in normalized aggregated data records in someembodiments of the EXDB, e.g., a Data Field Recognition (“DFR”)component 1500;

FIG. 16 shows a logic flow diagram illustrating example aspects ofclassifying entity types in some embodiments of the EXDB, e.g., anEntity Type Classification (“ETC”) component 1600;

FIG. 17 shows a logic flow diagram illustrating example aspects ofidentifying cross-entity correlation in some embodiments of the EXDB,e.g., a Cross-Entity Correlation (“CEC”) component 1700;

FIG. 18 shows a logic flow diagram illustrating example aspects ofassociating attributes to entities in some embodiments of the EXDB,e.g., an Entity Attribute Association (“EAA”) component 1800;

FIG. 19 shows a logic flow diagram illustrating example aspects ofupdating entity profile-graphs in some embodiments of the EXDB, e.g., anEntity Profile-Graph Updating (“EPGU”) component 1900;

FIG. 20 shows a logic flow diagram illustrating example aspects ofgenerating search terms for profile-graph updating in some embodimentsof the EXDB, e.g., a Search Term Generation (“STG”) component 2000;

FIGS. 21A-E show user interface diagrams illustrating example featuresof user interfaces for an electronic virtual wallet in some embodimentsof the EXDB;

FIG. 22 shows a block diagram illustrating example aspects of a merchantanalytics platform in some embodiments of the EXDB;

FIGS. 23A-B show data flow diagrams illustrating an example procedure toprovide a user and/or merchant offers for products, services and/or thelike, using user behavior patterns derived from card-based transactiondata in some embodiments of the EXDB;

FIG. 24 shows a logic flow diagram illustrating example aspects ofproviding a user and/or merchant offers for products, services and/orthe like, using user behavior patterns derived from card-basedtransaction data in some embodiments of the EXDB, e.g., a MerchantAnalytics (“MA”) component;

FIG. 25 shows a logic flow diagram illustrating example aspects ofgenerating a user behavior pattern analysis in some embodiments of theEXDB, e.g., a User Behavioral Pattern Analytics (“UBPA”) component;

FIG. 26 shows a logic flow diagram illustrating example aspects ofidentifying user behavioral patterns from aggregated card-basedtransaction data in some embodiments of the EXDB, e.g., a User PatternIdentification (“UPI”) component;

FIGS. 27A-B show block diagrams illustrating example aspects of merchantanalytics in a second set of embodiments of the EXDB;

FIGS. 28A-C show data flow diagrams illustrating an example procedurefor econometrical analysis of a proposed investment strategy based oncard-based transaction data in some embodiments of the EXDB;

FIG. 29 shows a logic flow diagram illustrating example aspects ofnormalizing raw card-based transaction data into a standardized dataformat in some embodiments of the EXDB, e.g., a Transaction DataNormalization (“TDN”) component;

FIG. 30 shows a logic flow diagram illustrating example aspects ofgenerating classification labels for card-based transactions in someembodiments of the EXDB, e.g., a Card-Based Transaction Classification(“CTC”) component;

FIG. 31 shows a logic flow diagram illustrating example aspects offiltering card-based transaction data for econometrical investmentstrategy analysis in some embodiments of the EXDB, e.g., a TransactionData Filtering (“TDF”) component;

FIG. 32 shows a logic flow diagram illustrating example aspects ofanonymizing consumer data from card-based transactions for econometricalinvestment strategy analysis in some embodiments of the EXDB, e.g., aConsumer Data Anonymization (“CDA”) component;

FIGS. 33A-B show logic flow diagrams illustrating example aspects ofeconometrically analyzing a proposed investment strategy based oncard-based transaction data in some embodiments of the EXDB, e.g., anEconometrical Strategy Analysis (“ESA”) component;

FIG. 34 shows a logic flow diagram illustrating example aspects ofreporting business analytics derived from an econometrical analysisbased on card-based transaction data in some embodiments of the EXDB,e.g., a Business Analytics Reporting (“BAR”) component;

FIG. 35 shows a logic flow diagram illustrating example aspects ofsharing an analytical model generated using data acquired using thecentralized personal information platform in some embodiments of theEXDB, e.g., an Analytical Model Sharing (“AMS”) component;

FIG. 36 shows a logic flow diagram illustrating example aspects of ametadata based interpretation engine of the EXDB that generatesstandardized encryptmatics XML from structured data obtained fromvarious sources via the centralized personal information platform, e.g.,an Encryptmatics XML Converter (“EXC”) component;

FIG. 37 shows a data flow diagram illustrating an example email dataaggregation procedure, in one embodiment of the EXDB;

FIG. 38 shows a block diagram illustrating an example distributedlinking node mesh, in one embodiment of the EXDB;

FIGS. 39A-F show a block diagram illustrating an example distributedlinking node mesh search, in one embodiment of the EXDB;

FIGS. 40A-C show a block diagram illustrating an example distributedlinking node mesh index creation, in one embodiment of the EXDB;

FIG. 41 shows a logic flow illustrating an example Encryptmatics XMLConverter component, in one embodiment of the EXDB;

FIG. 42 shows a logic flow illustrating input language loading by anEncryptmatics XML Converter component, in one embodiment of the EXDB;

FIGS. 43A-B show a logic flow illustrating input model conversion by anEncryptmatics XML Converter component, in one embodiment of the EXDB;

FIG. 44 shows a block diagram illustrating aspects of a tumblar datasource manipulation/anonymization component, e.g., a TDS component, inone embodiment of the EXDB;

FIG. 45 shows a logic flow diagram illustrating an example tumblar datasource manipulation/anonymization component, in one embodiment of theEXDB;

FIG. 46 shows an example data flow illustrating mesh aggregation andcluster querying, in one embodiment of a EXDB;

FIG. 47 shows an example logic flow illustrating cluster responseanalysis and transaction triggering, in one embodiment of a EXDB;

FIG. 48A-C illustrate an example EXDB application embodiment, in oneembodiment of the EXDB; and

FIG. 49 shows a block diagram illustrating embodiments of a EXDBcontroller.

The leading number of each reference number within the drawingsindicates the figure in which that reference number is introduced and/ordetailed. As such, a detailed discussion of reference number 101 wouldbe found and/or introduced in FIG. 1. Reference number 201 is introducedin FIG. 2, etc. The leading number of each reference number within thedrawings indicates the figure in which that reference number isintroduced and/or detailed. As such, a detailed discussion of referencenumber 101 would be found and/or introduced in FIG. 1. Reference number201 is introduced in FIG. 2, etc.

DETAILED DESCRIPTION EXDB

The MULTI-SOURCE, MULTI-DIMENSIONAL, CROSS-ENTITY, MULTIMEDIAENCRYPTMATICS DATABASE PLATFORM APPARATUSES, METHODS AND SYSTEMS(hereinafter “EXDB”) transform data aggregated from various computerresources, via EXDB components, into updated entity profiles, socialgraphs and/or investment recommendations. The EXDB components, invarious embodiments, implement advantageous features as set forth below.

Centralized Personal Information Platform

FIG. 1 shows a block diagram illustrating example aspects of acentralized personal information platform in some embodiments of theEXDB. In various scenarios, originators 111 such as merchants nib,consumers 111 c (including, e.g., social networking sites), accountissuers, acquirers ma, and/or the like, desire to utilize informationfrom payment network systems for enabling various features forconsumers, and may provide input for the generation of a centralizedpersonal information platform.

For all of the input types (e.g., consumer transactions nib, socialnetwork interactions 111 d (e.g., emails, reviews, text posts, photos,audio/video/multimedia, conversations, chats, etc.), financialinstitution activity ma (e.g., acquirers, authorizations, denials,issuers, fraud detection, etc.), merchant activities nib (e.g., offers,coupons, redemptions, etc.), and/or the like, the mesh server 105 mayaggregate and store such inputs in consolidated database 104 b.

The mesh server aggregation may be achieved by obtaining a feed offinancial transactions (e.g., if the mesh server is also a pay networkserver), by obtaining complete feed access (e.g., firehose feeds), fromsocial networks (e.g., Facebook, Twitter, etc.), using publicallyavailable data API's (e.g., Google search API), and/or the like.

In one embodiment, the feeds may be obtained via high-bandwidth networkconnections. An example of the high-bandwidth network connections mayinclude multiple optical fiber connections to an Internet backplane suchas the multinational Equinix Exchange, New York International InterneteXchange (e.g., “NYIIX”), and/or the like.

The obtained feeds may be stored in fast storage array servers forprocessing or access by other processing servers. Examples of the faststorage array servers may include server blades such as thosemanufactured by Dell Computer (e.g., Dell model M820, M620, and/or thelike), having multiple RAID fast SSD drives of type SAS with memorycache of type L1, L2, L3, and/or the like. In another embodiment, thefeeds may be stored in a public cloud storage service (e.g., Amazon S3,and/or the like) or private cloud (e.g., OpenStack Storage object and/orOpenStack Storage block storage running on servers such as thosedescribed above).

In one embodiment, the fast storage servers may employ a distributedfile system that provides high-throughput access to stored data. Examplefile systems suitable for this purpose may include the HadoopDistributed File System (e.g., “HDFS”), Google BigTable, and/or thelike. The file system may be implemented substantially as a key/valuestore or, in other embodiments, as a structured file system containingdirectories and files. In some embodiments, a hybrid key/valuestructured file system may be used in order to utilize the capabilitiesof both a key/value store and a structured file system. In oneembodiment, the fast storage array servers may be connected to one ormesh servers (e.g., 105) for feed processing.

In one embodiment, the mesh servers (e.g., 105) may be server bladessuch as those described above. In another embodiment, the servers may bevirtualized and running on a private virtualization platform such asVMWare ESXi, Xen, OpenStack Compute and/or the like. In still otherembodiments, the servers may be virtualized using a publically availablecloud service such as Amazon EC2 (e.g., via an Amazon MachineImage/“AMI”, and/or the like) or Rackspace (e.g., by providing a machineimage such as a VDI or OVA file suitable for creating a virtualizedmachine).

The mesh server may generate dictionary short code words for every typeof input and associate with that short word with the input (e.g., a MD5hash, etc. may generate a short word for every type of input, where theresulting short code is unique to each input). This short code to actualdata input association, when aggregated, may form the basis of a meshdictionary. An example of mesh dictionary entry substantially in thefollowing form of XML is:

<dictionary_entry>  {id: “1h65323765gtyuf#uy76355”, type: email,category: {cat1: “food”, cat2: “dinner”}, from_addr:“john.doe@gmail.com”, to_addr: “jane.doe@gmail.com”, subject: “KoreanBBQ this weekend?”, dictionary_keywords: “Korean, dinner, nyc”,content_hash: “7m865323476feeaniiji”} </dictionary_entry>

Segmented portions, complete dictionaries, and/or updates thereto, maythus be sent en masse to mesh analytics clone servers; for example, suchupdate may be done at off-network peak hours set to occur at dynamicallyand/or at set intervals. This allows the analytics servers to performanalytics operations, and it allows those analytics servers to operateon short codes even without the full underlying backend data beingavailable. In so doing, dictionaries may be analyised using less spacethan the full underlying raw data would require. Additionally,dictionaries may be distributed between multiple servers. In oneembodiment, the dictionaries are replicated across multiple servers and,periodically, synchronized. In one embodiment, any inconstancies indistributed and/or separated dictionaries may be reconciled usingdemarcation protocol and/or controlled inconsistency reconciliation forreplicated data (see D. Barbara H. Garcia-Molina, The case forcontrolled inconsistency in replicated data,” Proc. of the Workshop onManagement of Replicated Data, Houston, Tex., November 1990; D. BarbaraH. Garcia-Molina, The demarcation protocol a technique for maintainingarithmetic constraints in distributed database systems, CS-TR-320-91,Princeton University, April 1991; the contents of both which are hereinexpressly incorporated by reference). In one embodiment, dictionariesmay defer any analytic operations that require the backend data untilwhen the caching of the dictionary is complete. It should be noted thatthroughout this disclosure, reference is made to “payment networkserver” or “pay network server.” It should be understood that suchserver may incorporate mesh servers, and it also contemplates that suchmesh servers may include a network of mesh analytics clone servers,clustering node servers, clustering servers, and/or the like.

Features that entities may desire include application services 112 suchas alerts 112 a, offers 112C, money transfers 112 n, fraud detection 112b, and/or the like. In some embodiments of the EXDB, such originatorsmay request data to enable application services from a common, secure,centralized information platform including a consolidated, cross-entityprofile-graph database 101. For example, the originators may submitcomplex queries to the EXDB in a structure format, such as the examplebelow. In this example, the query includes a query to determine alocation (e.g., of a user), determine the weather associated with thelocation, perform analyses on the weather data, and provide an explodedgraphical view of the results of the analysis:

<int Model_id =“1” environment_type=“RT”meta_data=“./fModels/robotExample.meta”tumblar_location=“./fModels/robotExample.tumblar.location”input_format=“JSON” pmmls=“AUTONOMOUS_AGENTS.PMML” Model_type=“AUTONOMOUS_AGENTS” > <vault > <door:LOCATION> <lock name=“DETERMINELOCATION” inkey=“INPUT” inkeyname=“lat” inkey2=“INPUT” inkeyname2=“long”function=“ROUND” fnct1-prec=“−2” function-1=“JOIN” fnct2-delim=“:”tumblar=‘LAT_LONG.Key’ outkey=“TEMP” out keyname=“location”type=“STRING” /> <lock name=“DETERMINE WEATHER” inkey=“TEMP”inkeyname=“location” mesh=‘MESHRT.RECENTWEATHER’ mesh-query=‘HASH’outkey=“TEMP” outkeyname=“WEATHERDATA” type=“ARRAY” /> <lockname=“EXPLODE DATA” inkey=“TEMP” inkeyname=“WEATHERDATA”function=“EXPLODE” fnct-delim=“:” outkey=“MODELDATA” outkeystartindex=1/> <lock name=“USER SETTINGS” inkey=“INPUT” inkeyname=“USERID”mesh=‘MESHRT.AUTONOMOUSAGENT.SETTINGS’ mesh-query=‘HASH’ outkey=“TEMP”outkeyname=“USERSETTINGS” type=“ARRAY” /> <lock name=“EXPLODE USER”inkey=“TEMP” inkeyname=“USERSETTINGS” function=“EXPLODE” fnct-delim=“:”outkey=“USERDATA” outkeystartindex=1 /> <lock name=“RUN MODELE”inkey=“MODELDATA” inkey1=“USERDATA” function=“TREE”fnc-pmml=“AUTONOMOUS_AGENTS.PMML” outkey=“OUTPUT” outkeyname=“WEATHER”type=“NUMERIC” /> </door> </vault>

A non-limiting, example listing of data that the EXDB may return basedon a query is provided below. In this example, a user may log into awebsite via a computing device. The computing device may provide a IPaddress, and a timestamp to the EXDB. In response, the EXDB may identifya profile of the user from its database, and based on the profile,return potential merchants for offers or coupons:

-------------------------------------------------- ------------------Use Case 3 ------------------- -- User log into a website -- Only IPaddress, GMT and day of week is passed to Mesh -- Mesh matches profilebased on Affinity Group -- Mesh returns potential Merchants for offersor coupons based on tempory model using suppression rules-------------------------------------------------- -- Test case 1IP:24:227:206 Hour:9 Day:3 -- Test case 2 IP:148:181:75 Hour:4 Day:5-------------------------------------------------- ------- AffinityGroupLookup --------------------------------------------------------------------- Look up test case 1[OrderedDict([(‘ISACTIVE’, ‘True’), (‘ENTITYKEY’, ‘24:227:206:3:1’),(‘XML’, None), (‘AFFINITYGROUPNAME’, ‘24:227:206:3:1’), (‘DESCRIPTION’,None), (‘TYPEOF’, None), (‘UUID’, ‘5f8df970b9ff11e09ab9270cf67eca90’)]),OrderedDict([(‘ISACTIVE’, ‘True’), (‘BASEUUID’,‘4fbea327b9ff11e094f433b5d7c45677’), (‘TOKENENTITYKEY’,‘4fbea327b9ff11e094f433b5d7c45677:TOKEN:349:F’), (‘BASETYPE’,‘MODEL_002_001_00’), (‘STATUS’, ‘ACTIVE’), (‘ISSUEDDATE’, None),(‘WEIGHT’, ‘349’), (‘CATEGORY’, ‘F’), (‘DOUBLELINKED’, None), (‘UUID’,‘6b6aab39b9ff11e08d850dc270e3ea06’)]), OrderedDict([(‘ISACTIVE’,‘True’), (‘BASEUUID’, ‘4fbea328b9ff11e0a5f833b5d7c45677’),(‘TOKENENTITYKEY’, ‘4fbea328b9ff11e0a5f833b5d7c45677:TOKEN:761:1’),(‘BASETYPE’, ‘MODEL_003_001_00’), (‘STATUS’, ‘ACTIVE’), (‘ISSUEDDATE’,None), (‘WEIGHT’,‘761’), (‘CATEGORY’, ‘1’), (‘DOUBLELINKED’, None),(‘UUID’, ‘68aaca40b9ff11e0ac799fd4e415d9de’)]),OrderedDict([(‘ISACTIVE’, ‘True’), (‘BASEUUID’,‘4fbea328b9ff11e0a5f833b5d7c45677’), (‘TOKENENTITYKEY’,‘4fbea328b9ff11e0a5f833b5d7c45677:TOKEN:637:2’), (‘BASETYPE’,‘MODEL_003_001_00’), (‘STATUS’, ‘ACTIVE’), (‘ISSUEDDATE’, None),(‘WEIGHT’,637’), (‘CATEGORY’, ‘2’), (‘DOUBLELINKED’, None),(‘UUID’,6b6d1c38b9ff11e08ce10dc270e3ea06’)]), OrderedDict([(‘ISACTIVE’,‘True’), (‘BASEUUID’, ‘4fbea328b9ff11e0a5f833b5d7c45677’),(‘TOKENENTITYKEY’, ‘4fbea328b9ff11e0a5f833b5d7c45677:TOKEN:444:3’),(‘BASETYPE’, ‘MODEL_003_001_00’), (‘STATUS’, ‘ACTIVE’), (‘ISSUEDDATE’,None), (‘WEIGHT’, ‘444’), (‘CATEGORY’, ‘3’), (‘DOUBLELINKED’, None),(‘UUID’,6342aa53b9ff11e0bcdb9fd4e415d9de’)]), OrderedDict([(‘ISACTIVE’,‘True’), (‘BASEUUID’, ‘4fbea328b9ff11e0a5f833b5d7c45677’),(‘TOKENENTITYKEY’, ‘4fbea328b9ff11e0a5f833b5d7c45677:TOKEN:333:4’),(‘BASETYPE’, ‘MODEL_003_001_00’), (‘STATUS’, ‘ACTIVE’), (‘ISSUEDDATE’,None), (‘WEIGHT’, ‘333’), (‘CATEGORY’, ‘4’), (‘DOUBLELINKED’, None),(‘UUID’, ‘62bd26a2b9ff11e0bc239fd4e415d9de’)]),OrderedDict([(‘ISACTIVE’, ‘True’), (‘BASEUUID’,‘4fbea328b9ff11e0a5f833b5d7c45677’), (‘TOKENENTITYKEY’,‘4fbea328b9ff11e0a5f833b5d7c45677:TOKEN:307:5’), (‘BASETYPE’,‘MODEL_003_001_00’), (‘STATUS’, ‘ACTIVE’), (‘ISSUEDDATE’, None),(‘WEIGHT’, ‘307’), (‘CATEGORY’, ‘5’), (‘DOUBLELINKED’, None), (‘UUID’,‘6b6d1c39b9ff11e0986c0dc270e3ea06’)]), OrderedDict([(‘ISACTIVE’,‘True’), (‘BASEUUID’, ‘4fbea32db9ff11e09f3e33b5d7c45677’),(‘TOKENENTITYKEY’, ‘4fbea32db9ff11e09f3e33b5d7c45677:TOKEN:801:Spend’),(‘BASETYPE’, ‘MODEL_008_001_00’), (‘STATUS’, ‘ACTIVE’), (‘ISSUEDDATE’,None), (‘WEIGHT’, ‘801’), (‘CATEGORY’, ‘Spend), (‘DOUBLELINKED’, None),(‘UUID’, ‘6b6d1c3ab9ff11e0a4ec0dc270e3ea06’)]),OrderedDict([(‘ISACTIVE’, ‘True’), (‘BASEUUID’,‘4fbea32eb9ff11e0b55133b5d7c45677’), (‘TOKENENTITYKEY’,‘4fbea32eb9ff11e0b55133b5d7c45677:TOKEN:1:Volume’), (‘BASETYPE’,‘MODEL_009_001_00’), (‘STATUS’, ‘ACTIVE’), (‘ISSUEDDATE’, None),(‘WEIGHT’, ‘1’), (‘CATEGORY’, ‘Volume’), (‘DOUBLELINKED’, None),(‘UUID’, ‘62a09df3b9ff11e090d79fd4e415d9de’)])] Found a direct match148:181:75:1:2 -- Failed to find a direct match -- Try again with onlyIP address and hour [OrderedDict([(‘ISACTIVE’, ‘True’), (‘ENTITYKEY’,‘148:181:75:1:1’), (‘XML’, None), (‘AFFINITYGROUPNAME’,‘148:181:75:1:1’), (‘DESCRIPTION’, None), (‘TYPEOF’, None)])] -- Foundmatch for case 2----------------------------------------------------------------------------- Temporary model rules ------------------------------------------------------------------------------ {1:{‘LOWER’: 10, ‘BASETYPE’: [‘MODEL_002_001_00 ’, ‘MODEL_003_001_00’],‘attribute’: ‘WEIGHT’, ‘rule’: ‘NEAR’, ‘OP’: ‘PROX’, ‘type’:‘TOKENENTITY’, ‘HIGHER’: 10}, 2: {‘type’: [‘MERCHANT’], ‘rule’:‘FOLLOW’}, 3: {‘rule’: ‘RESTRICTSUBTYPE’, ‘BASETYPE’:[‘MODEL_002_001_00’, ‘MODEL_003_001_00’]}}----------------------------------------------------------------------------- Temporary Model Output ------------------------------------- For Use Case 1 -------------------------------------------------------------------------------- -- Number ofNodes:102      LIVRARIASICILIAN          GDPCOLTD    GOODWILLINDUSTRIES        DISCOUNTDE        BARELANCHOE       BLOOMINGDALES     PARCWORLDTENNIS      STRIDERITEOUTLET         PARCCEANOR         PONTOFRIO        FNACPAULISTA          FINISHLINE     WALMARTCENTRAL      BESNIINTERLARGOS     PARCLOJASCOLOMBO      SHOPTIMEINTER      BEDBATHBEYOND         MACYSWEST   PARCRIACHUELOFILIAL      JCPENNEYCORPINC     PARCLOJASRENNERFL  PARCPAQUETAESPORTES          MARISALJ    PARCLEADERMAGAZINE        INTERFLORA         DECATHLON      PERNAMBUCANASFL        KARSTADTDE        PARCCEAMCO           CHAMPS        ACCESSORIZE   BLOOMINGDALESDVRS   PARCLIVRARIACULTURA        PARCCEALOJA      ARQUIBANCADA            KITBAG     FREDERICKSOFHLWD         WALMART    PARCLOJASINSINUANTE     WALMARTCONTAGEM        FOOTLOCKER      PARCSANTALOLLA       RICARDOELETRO      PARCPONTOFRIO      DOTPAYPLPOLSKA          CAMICADO         KARSTADT        PARCRAMSONS        PARCGREGORY        GREMIOFBPA         WALMARTSJC    PRODIRECTSOCCERLTD        LAVIEENROSE        PARCMARISALJ           ORDERS   PARCNSNNATALNORTE      LOJASINSINUANTE               B        CITYCOUNTY     WALMARTPACAEMBU             SOHO     WALMARTOSASCO      FOSSILSTORESIINC        MENARDSCLIO       PARCPEQUENTE            BEALLS       THEHOMEDEPOT           VIAMIA    PARCLOJASRIACHUELO      PARCLOJASMILANO        NORDSTROM    WAILANACOFFEEHOUSE       LANCHOEBELLA           PUKET     WALMARTSTORESINC   PARCPERNAMBUCANASFL      SMARTSHOPPER   PARCMAGAZINELUIZASP  COLUMBIASPORTSWEARCO     BARELANCESTADA         DONATEEBAY    PARCRICARDOELETRO      PARCDISANTINNI         SCHUHCOUK           CEANOR      PARCCAMICADO      PARCCENTAUROCE      PARCMARLUIJOIAS         ALBADAH          MARTINEZ     MONEYBOOKERSLTD            MACYS      PARCRIOCENTER      PARCCASASBAHIA     PARCSUBMARINOLOJA             INC       SUBMARINOLOJA       LOJASRENNERFL     RIACHUELOFILIAL      PARCSONHODOSPES           PINKBIJU       PARCCEAMRB----------------------------------------------------------------------------- Temporary model Output ------------------------------------ For Use Case 2 -------------------------------------------------------------------------------- -- Number ofNodes:3            KITBAG  COLUMBIASPORTSWEARCO         GREMIOFBPA-------------------------------------------------------------- --------End of Example Use Case -----------------------------------------------------------------

In some embodiments, the EXDB may provide access to information on aneed-to-know basis to ensure the security of data of entities on whichthe EXDB stores information. Thus, in some embodiments, access toinformation from the centralized platform may be restricted based on theoriginator as well as application services for which the data isrequested. In some embodiments, the EXDB may thus allow a variety offlexible application services to be built on a common databaseinfrastructure, while preserving the integrity, security, and accuracyof entity data. In some implementations, the EXDB may generate, update,maintain, store and/or provide profile information on entities, as wellas a social graph that maintains and updates interrelationships betweeneach of the entities stored within the EXDB. For example, the EXDB maystore profile information on an issuer bank 102 a (see profile 103 a), aacquirer bank 102 b (see profile 103 b), a consumer 102C (see profile103 c), a user 102 d (see profile 103 d), a merchant 102 e (see profile103 e), a second merchant 102 f (see profile 1030. The EXDB may alsostore relationships between such entities. For example, the EXDB maystore information on a relationship of the issuer bank 102 a to theconsumer 102C shopping at merchant 102 e, who in turn may be related touser 102 d, who might bank at the back 102 b that serves as acquirer formerchant 102 f.

FIGS. 2A-F show block diagrams illustrating example aspects of datamodels within a centralized personal information platform in someembodiments of the EXDB. In various embodiments, the EXDB may store avariety of attributes of entities according to various data models. Afew non-limiting example data models are provided below. In someembodiments, the EXDB may store user profile attributes. For example, auser profile model may store user identifying information 201, useraliases 202, email addresses 203, phone numbers 204, addresses 205,email address types 206, address types 207, user alias types 208,notification statuses 209, ISO country 210, phone number types 211,contract information with the EXDB 212, user authorization status 213,user profile status 214, security answer 215, security questions 216,language 217, time zone 218, and/or the like, each of the above fieldtypes including one or more fields and field values. As another example,a user financial attributes model may store user identifying information220, user financial account information 221, account contractinformation 222, user financial account role 223, financial account type224, financial account identifying information 225, contract information226, financial account validation 227, financial account validation type228, and/or the like. As another example, a user payment card attributesdata model may include field types such s, but not limited to: useridentifying information 230, user financial account information 231,user financial account role 232, account consumer applications 233, userconsumer application 234, financial account type 235, financial accountvalidation type 236, financial account information 237, consumerapplication information 238, consumer application provider information239, and/or the like. As another example, a user services attributesdata model may include field types such as, but not limited to: useridentifying information 240, user alias 241, consumer application useralias status 242, user alias status 243, status change reason code 244,user contract 245, contract information 246, user service attributevalue 247, consumer application attributes 248, account serviceattribute value, account contract 250, user profile status 261, contractbusiness role 252, contract business 253, client information 254,contract role 255, consumer application 256, user activity audit 257,login results 258, and/or the like. As another example, a user servicesusage attributes data model may include field types such as, but notlimited to: user identifying information 260, user alias 261, consumerapplication user alias status 262, status change reason code 263, useralias status 264, user consumer application 265, user login audit 266,login result 267, account service attribute value 268, account consumerapplication 269, consumer application 270, consumer application provider271, login result 272, and/or the like. As another example, a user graphattributes data model may include field types such as, but not limitedto: user identifying information 280, user contact 281, consumerapplication user alias status 282, relationship 283, and/or the like. Insome embodiments, the EXDB may store each object (e.g., user, merchant,issuer, acquirer, IP address, household, etc.) as a node in graphdatabase, and store data with respect to each node in a format such asthe example format provided below:

<Nodes Data> ID,Nodes,Label2fdc7e3fbd1c11e0be645528b00e8d0e,2fdc7e3fbd1c11e0be645528b00e8d0e,AFFINITYGROUPNAME:49:95:0:3:132b1d53ebd1c11e094172557fb829fdf,32b1d53ebd1c11e094172557fb829fdf,TOKENENTITYKEY:2b8494f0bd1c11e09c856d888c43f7c2:TOKEN:0:F2e6381e4bd1c11e0b9ffc929a54bb0fd,2e6381e4bd1c11e0b9ffc929a54bb0fd,MERCHANTNAME:        MERCHANT_ABC2fdc7e3dbd1c11e0a22d5528b00e8d0e,2fdc7e3dbd1c11e0a22d5528b00e8d0e,AFFINITYGROUPNAME:49:95:0:1:12e6381e7bd1c11e091b7c929a54bb0fd,2e6381e7bd1c11e091b7c929a54bb0fd,MERCHANTNAME:        MERCHANT_XYZ2cf8cbabbd1c11e0894a5de4f9281135,2cf8cbabbd1c11e0894a5de4f9281135,USERNAME:000060FF6557F1032e6381debd1c11e0b336c929a54bb0fd,2e6381debd1c11e0b336c929a54bb0fd,MERCHANTNAME:        MERCHANT_1232e6381e0bd1c11e0b4e8c929a54bb0fd,2e6381e0bd1c11e0b4e8c929a54bb0fd,MERCHANTNAME:        MERCHANT_FGH2cf681c1bd1c11e0b8815de4f9281135,2cf681c1bd1c11e0b8815de4f9281135,USERNAME:000030C57080FFE82b8494f1bd1c11e0acbd6d888c43f7c2,2b8494f1bd1c11e0acbd6d888c43f7c2,MODELNAME:MODEL_003_001_0032b44638bd1c11e0b01c2557fb829fdf,32b44638bd1c11e0b01c2557fb829fdf,TOKENENTITYKEY:2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:12fdc7e40bd1c11e094675528b00e8d0e,2fdc7e40bd1c11e094675528b00e8d0e,AFFINITYGROUPNAME:49:95:0:4:12b8494f0bd1c11e09c856d888c43f7c2,2b8494f0bd1c11e09c856d888c43f7c2,MODELNAME:MODEL_002_001_0032b44639bd1c11e0b15b2557fb829fdf,32b44639bd1c11e0b15b2557fb829fdf,TOKENENTITYKEY:2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:232ce84febd1c11e0b0112557fb829fdf,32ce84febd1c11e0b0112557fb829fdf,TOKENENTITYKEY:2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:42e6381e3bd1c11e095b1c929a54bb0fd,2e6381e3bd1c11e095b1c929a54bb0fd,MERCHANTNAME:        MERCHANT_78934582a87bd1c11e080820167449bc60f,34582a87bd1c11e080820167449bc60f,TOKENENTITYKEY:2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:778:52e6381e5bd1c11e0b62cc929a54bb0fd,2e6381e5bd1c11e0b62cc929a54bb0fd,MERCHANTNAME:        MERCHANT_4562fdc7e3ebd1c11e088b55528b00e8d0e,2fdc7e3ebd1c11e088b55528b00e8d0e,AFFINITYGROUPNAME:49:95:0:2:132c4e80dbd1c11e09e442557fb829fdf,32c4e80dbd1c11e09e442557fb829fdf,TOKENENTITYKEY:2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:774:52e6381e1bd1c11e0bf28c929a54bb0fd,2e6381e1bd1c11e0bf28c929a54bb0fd,MERCHANTNAME:        MERCHANT_WER2cf681b8bd1c11e08be85de4f9281135,2cf681b8bd1c11e08be85de4f9281135,USERNAME:00002552FC930FF82cf8cba8bd1c11e09fbc5de4f9281135,2cf8cba8bd1c11e09fbc5de4f9281135,USERNAME:0000570FF1B46A2432b4463abd1c11e0bdaa2557fb829fdf,32b4463abd1c11e0bdaa2557fb829fdf,TOKENENTITYKEY:2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:32cf8cbaebd1c11e0b6515de4f9281135,2cf8cbaebd1c11e0b6515de4f9281135,USERNAME:000064A20FF962D42e6381e6bd1c11e08087c929a54bb0fd,2e6381e6bd1c11e08087c929a54bb0fd,MERCHANTNAME:        MERCHANT_4962e6381e2bd1c11e0941dc929a54bb0fd,2e6381e2bd1c11e0941dc929a54bb0fd,MERCHANTNAME:        MERCHANT_SDF <Edge Data>Source,Target,Type,label,Weight 32ce84febd1c11e0b0112557fb829fdf,2e6381e6bd1c11e08087c929a54bb0fd,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:4,10002fdc7e3ebd1c11e088b55528b00e8d0e,32ce84febd1c11e0b0112557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:4,1 0002e6381e2bd1c11e0941dc929a54bb0fd,34582a87bd1c11e080820167449bc60f,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:778:5,77 82b8494f1bd1c11e0acbd6d888c43f7c2,34582a87bd1c11e080820167449bc60f,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:778:5,77 82e6381e1bd1c11e0bf28c929a54bb0fd,32b44639bd1c11e0b15b2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:2,02e6381e0bd1c11e0b4e8c929a54bb0fd,32ce84febd1c11e0b0112557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:4,1 00032b44639bd1c11e0b15b2557fb829fdf,2e6381e6bd1c11e08087c929a54bb0fd,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:2,02e6381e1bd1c11e0bf28c929a54bb0fd,32ce84febd1c11e0b0112557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:4,1 0002e6381debd1c11e0b336c929a54bb0fd,32ce84febd1c11e0b0112557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:4, 10002e6381e3bd1c11e095b1c929a54bb0fd,34582a87bd1c11e080820167449bc60f,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:778:5,77 82fdc7e40bd1c11e094675528b00e8d0e,32b44639bd1c11e0b15b2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:2,02b8494f1bd1c11e0acbd6d888c43f7c2,32b4463abd1c11e0bdaa2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:3,02e6381e3bd1c11e095b1c929a54bb0fd,32b4463abd1c11e0bdaa2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:3,02e6381e3bd1c11e095b1c929a54bb0fd,32b1d53ebd1c11e094172557fb829fdf,MODEL_002_001_00,2b8494f0bd1c11e09c856d888c43f7c2:TOKEN:0:F,02e6381e5bd1c11e0b62cc929a54bb0fd,34582a87bd1c11e080820167449bc60f,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:778:5,77 82cf8cbabbd1c11e0894a5de4f9281135,32b44638bd1c11e0b01c2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:1,1 0002cf681b8bd1c11e08be85de4f9281135,32b1d53ebd1c11e094172557fb829fdf,MODEL_002_001_00,2b8494f0bd1c11e09c856d888c43f7c2:TOKEN:0:F,032b4463abd1c11e0bdaa2557fb829fdf,2e6381e6bd1c11e08087c929a54bb0fd,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:3,02e6381debd1c11e0b336c929a54bb0fd,32b44639bd1c11e0b15b2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:2,02e6381e1bd1c11e0bf28c929a54bb0fd,32b44638bd1c11e0b01c2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:1,1 0002e6381e5bd1c11e0b62cc929a54bb0fd,32ce84febd1c11e0b0112557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:4,1 0002e6381e1bd1c11e0bf28c929a54bb0fd,32b4463abd1c11e0bdaa2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:3,02e6381e2bd1c11e0941dc929a54bb0fd,32b44639bd1c11e0b15b2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:2,02b8494f1bd1c11e0acbd6d888c43f7c2,32c4e80dbd1c11e09e442557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:774:5,77 42e6381e2bd1c11e0941dc929a54bb0fd,32b1d53ebd1c11e094172557fb829fdf,MODEL_002_001_00,2b8494f0bd1c11e09c856d888c43f7c2:TOKEN:0:F,02e6381e4bd1c11e0b9ffc929a54bb0fd,32b4463abd1c11e0bdaa2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:3,02fdc7e3fbd1c11e0be645528b00e8d0e,32b4463abd1c11e0bdaa2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:3,02e6381e1bd1c11e0bf28c929a54bb0fd,32b1d53ebd1c11e094172557fb829fdf,MODEL_002_001_00,2b8494f0bd1c11e09c856d888c43f7c2:TOKEN:0:F,02fdc7e40bd1c11e094675528b00e8d0e,32ce84febd1c11e0b0112557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:4,1 0002cf8cba8bd1c11e09fbc5de4f9281135,32c4e80dbd1c11e09e442557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:774:5,77 42e6381e2bd1c11e0941dc929a54bb0fd,32b44638bd1c11e0b01c2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:1,1 0002e6381e4bd1c11e0b9ffc929a54bb0fd,32b1d53ebd1c11e094172557fb829fdf,MODEL_002_001_00,2b8494f0bd1c11e09c856d888c43f7c2:TOKEN:0:F,02e6381e5bd1c11e0b62cc929a54bb0fd,32b44639bd1c11e0b15b2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:2,032b1d53ebd1c11e094172557fb829fdf,2e6381e6bd1c11e08087c929a54bb0fd,MODEL_002_001_00,2b8494f0bd1c11e09c856d888c43f7c2:TOKEN:0:F,02b8494f1bd1c11e0acbd6d888c43f7c2,32b44639bd1c11e0b15b2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:2,02e6381e3bd1c11e095b1c929a54bb0fd,32b44638bd1c11e0b01c2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:1,1 0002fdc7e3dbd1c11e0a22d5528b00e8d0e,32ce84febd1c11e0b0112557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:4,1 0002cf681c1bd1c11e0b8815de4f9281135,32b44638bd1c11e0b01c2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:1, 10002cf681c1bd1c11e0b8815de4f9281135,32b1d53ebd1c11e094172557fb829fdf,MODEL_002_001_00,2b8494f0bd1c11e09c856d888c43f7c2:TOKEN:0:F,02e6381e3bd1c11e095b1c929a54bb0fd,32b44639bd1c11e0b15b2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:2,02fdc7e3fbd1c11e0be645528b00e8d0e,32b1d53ebd1c11e094172557fb829fdf,MODEL_002_001_00,2b8494f0bd1c11e09c856d888c43f7c2:TOKEN:0:F,032b44638bd1c11e0b01c2557fb829fdf,2e6381e6bd1c11e08087c929a54bb0fd,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:1,1 0002cf8cbaebd1c11e0b6515de4f9281135,32ce84febd1c11e0b0112557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:4, 10002e6381e6bd1c11e08087c929a54bb0fd,32b1d53ebd1c11e094172557fb829fdf,MODEL_002_001_00,2b8494f0bd1c11e09c856d888c43f7c2:TOKEN:0:F,02e6381e7bd1c11e091b7c929a54bb0fd,34582a87bd1c11e080820167449bc60f,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:778:5,77 82e6381e1bd1c11e0bf28c929a54bb0fd,34582a87bd1c11e080820167449bc60f,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:778:5,77 82e6381e5bd1c11e0b62cc929a54bb0fd,32b1d53ebd1c11e094172557fb829fdf,MODEL_002_001_00,2b8494f0bd1c11e09c856d888c43f7c2:TOKEN:0:F,02b8494f0bd1c11e09c856d888c43f7c2,32b1d53ebd1c11e094172557fb829fdf,MODEL_002_001_00,2b8494f0bd1c11e09c856d888c43f7c2:TOKEN:0:F,02b8494f1bd1c11e0acbd6d888c43f7c2,32b44638bd1c11e0b01c2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:1,1 0002e6381e6bd1c11e08087c929a54bb0fd,32b4463abd1c11e0bdaa2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:3,02b8494f1bd1c11e0acbd6d888c43f7c2,32ce84febd1c11e0b0112557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:4,1 0002cf681c1bd1c11e0b8815de4f9281135,32b44639bd1c11e0b15b2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:2,02cf681c1bd1c11e0b8815de4f9281135,32b4463abd1c11e0bdaa2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:3,02e6381e2bd1c11e0941dc929a54bb0fd,32b4463abd1c11e0bdaa2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:3,02e6381e3bd1c11e095b1c929a54bb0fd,32ce84febd1c11e0b0112557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:4,1 0002e6381e6bd1c11e08087c929a54bb0fd,32ce84febd1c11e0b0112557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:4,1 0002e6381e6bd1c11e08087c929a54bb0fd,34582a87bd1c11e080820167449bc60f,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:778:5,77 82e6381e6bd1c11e08087c929a54bb0fd,32b44638bd1c11e0b01c2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:1,1 0002fdc7e3ebd1c11e088b55528b00e8d0e,32b44639bd1c11e0b15b2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:2,02e6381e5bd1c11e0b62cc929a54bb0fd,32b4463abd1c11e0bdaa2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:3,02e6381e4bd1c11e0b9ffc929a54bb0fd,34582a87bd1c11e080820167449bc60f,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:778:5,77 82e6381e4bd1c11e0b9ffc929a54bb0fd,32b44638bd1c11e0b01c2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:1,1 00034582a87bd1c11e080820167449bc60f,2e6381e6bd1c11e08087c929a54bb0fd,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:778:5,77 82e6381e6bd1c11e08087c929a54bb0fd,32b44639bd1c11e0b15b2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:2,02e6381e5bd1c11e0b62cc929a54bb0fd,32b44638bd1c11e0b01c2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:1,1 0002fdc7e3fbd1c11e0be645528b00e8d0e,32b44638bd1c11e0b01c2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:1,1 0002cf681b8bd1c11e08be85de4f9281135,32b44639bd1c11e0b15b2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:2,02e6381e4bd1c11e0b9ffc929a54bb0fd,32b44639bd1c11e0b15b2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:2,02cf681b8bd1c11e08be85de4f9281135,32b4463abd1c11e0bdaa2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:3,02e6381e4bd1c11e0b9ffc929a54bb0fd,32ce84febd1c11e0b0112557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:4,1 0002e6381e2bd1c11e0941dc929a54bb0fd,32ce84febd1c11e0b0112557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:4,1 0002fdc7e3dbd1c11e0a22d5528b00e8d0e,32b44639bd1c11e0b15b2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:0:2,02cf681b8bd1c11e08be85de4f9281135,32b44638bd1c11e0b01c2557fb829fdf,MODEL_003_001_00,2b8494f1bd1c11e0acbd6d888c43f7c2:TOKEN:1000:1,1 000

In alternate examples, the EXDB may store data in a JavaScript ObjectNotation (“JSON”) format. The stored information may include dataregarding the object, such as, but not limited to: commands, attributes,group information, payment information, account information, etc., suchas in the example below:

{‘MERCHANT’: {‘TYPEOFTYPES’: [‘MERCHANTS’, ‘SYNTHETICNETWORKS’],‘FUNCTIONS’: {‘ENTITYCREATION’: ‘putNetwork’} , ‘UNIQUEATTIBUTES’:[‘MERCHANTNAME’], ‘TOKENENTITIESRELATIONSHIPS’: [ ],‘ATTRIBUTES’:{‘MERCHANT’: (2, ‘STRING’, 0, ‘VALUE’), ‘MERCH_ZIP_CD’: (7, ‘STRING’, 0,‘VALUE’), ‘MERCH_NAME’: (8, ‘STRING’, 0, ‘VALUE’), ‘MERCHANTNAME’: (3,‘STRING’, 0, ‘VALUE’), ‘ACQ_CTRY_NUM’: (4, ‘STRING’, 0, ‘VALUE’),‘ACQ_PCR’: (6, ‘STRING’, 0, ‘VALUE’), ‘ACQ_REGION_NUM’: (5, ‘STRING’, 0,‘VALUE’), ‘ISACTIVE’: (0, ‘BOOL’, 1, ‘VALUE’), ‘ENTITYKEY’: (1,‘STRING’,0, ‘VALUE’)} } , ‘AFFINITYGROUP’: {‘TYPEOFTYPES’: [‘AFFINITYGROUPS’],‘FUNCTIONS’: {‘ENTITYCREATION’: ‘putNetwork’} , ‘UNIQUEATTIBUTES’:[‘AFFINITYGROUPNAME’], ‘TOKENENTITIESRELATIONSHIPS’: [ ], ‘ATTRIBUTES’:{‘XML’: (2, ‘STRING’, 0, ‘VALUE’), ‘DESCRIPTION’: (4, ‘STRING’, 0,‘VALUE’), ‘ENTITYKEY’: (1, ‘STRING’, 0, ‘VALUE’), ‘TYPEOF’: (5,‘STRING’, 0, ‘VALUE’), ‘AFFINITYGROUPNAME’: (3, ‘STRING’, 0, ‘VALUE’),‘ISACTIVE’: (0, ‘BOOL’, 1, ‘VALUE’)} } , ‘CASCADINGPAYMENT’:{‘TYPEOFTYPES’: [‘CASCADINGPAYMENT’], ‘FUNCTIONS’: {‘ENTITYCREATION’:‘putNetwork’} , ‘UNIQUEATTIBUTES’: [‘CASCADINGPAYMENTNAME’],‘TOKENENTITIESRELATIONSHIPS’: [‘GROUP’], ‘ATTRIBUTES’: {‘STATUS’: (2,‘STRING’, 0, ‘VALUE’), ‘EXPDT’: (6, ‘DATETIME’, 0, ‘VALUE’), ‘GROUP’:(3, ‘STRING’, 0, ‘VALUE’), ‘RESTRICTIONS’: (7, ‘DICT’, 0, ‘VALUE’),‘CASCADINGPAYMENTNAME’: (4, ‘STRING’, 0, ‘VALUE’), ‘STARTDT’: (5,‘DATETIME’, 0, ‘VALUE’), ‘ISACTIVE’: (0, ‘BOOL’, 1, ‘VALUE’),‘ENTITYKEY’: (1, ‘STRING’, 0, ‘VALUE’)} } , ‘GROUP’: {‘TYPEOFTYPES’: [], ‘FUNCTIONS’: {‘ENTITYCREATION’: ‘putNetwork’} , ‘UNIQUEATTIBUTES’:[‘GROUPNAME’], ‘TOKENENTITIESRELATIONSHIPS’: { } , ‘ATTRIBUTES’:{‘GROUPNAME’: (2, ‘STRING’, 0, ‘VALUE’), ‘DESCRIPTION’: (2, ‘STRING’, 0,‘VALUE’), ‘ISACTIVE’: (0, ‘BOOL’, 1, ‘VALUE’), ‘ENTITYKEY’: (1,‘STRING’, 0, ‘VALUE’)} } , ‘USERS’: {‘TYPEOFTYPES’: [ ], ‘FUNCTIONS’:{‘ENTITYCREATION’: ‘putNetwork’} , ‘UNIQUEATTIBUTES’: [‘USERSID’],‘TOKENENTITIESRELATIONSHIPS’: { } , ‘ATTRIBUTES’: {‘USERSID’: (2,‘STRING’, 0, ‘VALUE’), ‘ISACTIVE’: (0, ‘BOOL’, 1, ‘VALUE’), ‘ENTITYKEY’:(1, ‘STRING’, 0, ‘VALUE’)} } , ‘TWITTERUSER’: {‘TYPEOFTYPES’:[‘TOKENENTITY’], ‘FUNCTIONS’: {‘ENTITYCREATION’: ‘putWGTNetwork’} ,‘UNIQUEATTIBUTES’: [‘USERNAME’], ‘TOKENENTITIESRELATIONSHIPS’: [‘USER’],‘ATTRIBUTES’: {‘USERNAME’: (2, ‘STRING’, 0, ‘VALUE’), ‘CITY’: (5,‘STRING’, 0, ‘VALUE’), ‘ENTITYKEY’: (1, ‘STRING’, 0, ‘VALUE’),‘USERLINK’: (6, ‘STRING’, 0, ‘VALUE’), ‘FULLNAME ’: (4, ‘STRING’, 0,‘VALUE’), ‘USERTAG’: (3, ‘STRING’, 0, ‘VALUE’), ‘ISACTIVE’: (0, ‘BOOL’,1, ‘VALUE’)} } , ‘COUPON’: {‘TYPEOFTYPES’: [‘COUPON’], ‘FUNCTIONS’:{‘ENTITYCREATION’: ‘putNetwork’} , ‘UNIQUEATTIBUTES’: [‘COUPONNAME’],‘TOKENENTITIESRELATIONSHIPS’: [‘MERCHANT’], ‘ATTRIBUTES’: {‘STATUS’: (2,‘STRING’, 0, ‘VALUE’), ‘MERCHANT’: (3, ‘STRING’, 0, ‘VALUE’), ‘TITLE’:(5, ‘STRING’, 0, ‘VALUE’), ‘NOTES’: (7, ‘STRING’, 0, ‘VALUE’),‘UPDATEDBY’: (11, ‘STRING’, 0, ‘VALUE’), ‘ENTITYKEY’: (1, ‘STRING’, 0,‘VALUE’), ‘DECRIPTION ’: (6, ‘STRING’, 0, ‘VALUE’), ‘CREATEDBY’: (10,‘STRING’, 0, ‘VALUE’) , ‘LASTUPDATEDT’: (9, ‘DATETIME’, 0, ‘VALUE’),‘EXPDT’: (13, ‘DATETIME’, 0, ‘VALUE’), ‘RESTRICTIONS’: (14, ‘DICT’, 0,‘VALUE’), ‘COUPONNAME ’: (4, ‘STRING’, 0, ‘VALUE’), ‘CREATIONDT’: (8,‘DATETIME’, 0, ‘VALUE’), ‘STARTDT’: (12, ‘DATETIME’, 0, ‘VALUE’),‘ISACTIVE’: (0, ‘BOOL’, 1, ‘VALUE’)} } , ‘MEMBERSHIP’: {‘TYPEOFTYPES ’:[‘MEMBERSHIPS’], ‘FUNCTIONS’: {‘ENTITYCREATION’: ‘putNetwork’} ,‘UNIQUEATTIBUTES’: [‘MEMBERSHIPNAME’], ‘TOKENENTITIESRELATIONSHIPS’:[‘MERCHANT’], ‘ATTRIBUTES’: {‘STATUS’: (2, ‘STRING’, 0, ‘VALUE’),‘MERCHANT’: (3, ‘STRING’, 0, ‘VALUE’), ‘RESTRICTIONS’: (7, ‘DICT’, 0,‘VALUE’), ‘MEMBERSHIPNAME’: (4, ‘STRING’, 0, ‘VALUE’), ‘STARTDT’: (5,‘DATETIME’, 0, ‘VALUE’), ‘EXPDT ’: (6, ‘DATETIME’, 0, ‘VALUE’),‘ISACTIVE’: (0, ‘BOOL’, 1, ‘VALUE’), ‘ENTITYKEY’: (1, ‘STRING’, 0,‘VALUE’)} } , ‘USERSECURITY’: {‘TYPEOFTYPES’: [‘SECURITY’], ‘FUNCTIONS’:{‘ENTITYCREATION’: ‘putNetwork’} , ‘UNIQUEATTIBUTES’:[‘USERSECURITYNAME’], ‘TOKENENTITIESRELATIONSHIPS’: [‘USER’],‘ATTRIBUTES’: {‘STATUS’: (2, ‘STRING’, 0, ‘VALUE’), ‘EXPDT’: (6,‘DATETIME’, 0, ‘VALUE’), ‘USERSECURITYNAME’: (4, ‘STRING’, 0, ‘VALUE’),‘USER’: (3, ‘STRING’, 0, ‘VALUE’), ‘RESTRICTIONS’: (7, ‘DICT’, 0,‘VALUE’), ‘STARTDT’: (5, ‘DATETIME’, 0, ‘VALUE’), ‘ISACTIVE’: (0,‘BOOL’, 1, ‘VALUE’), ‘ENTITYKEY’: (1, ‘STRING’, 0, ‘VALUE’)} } , ‘MCC’:{‘TYPEOFTYPES’: [‘MCC’], ‘FUNCTIONS’: {‘ENTITYCREATION’:‘putWGTNetwork’} , ‘UNIQUEATTIBUTES’: [‘MCCNAME’, ‘MCC’],‘TOKENENTITIESRELATIONSHIPS’: [‘MCCSEG’], ‘ATTRIBUTES’: {‘MCCSEG’: (4,‘STRING’, 0, ‘VALUE’), ‘MCC’: (2, ‘STRING’, 0, ‘VALUE’), ‘MCCNAME’: (3,‘STRING’, 0, ‘VALUE’), ‘ISACTIVE’: (0, ‘BOOL’, 1, ‘VALUE’), ‘ENTITYKEY’:(1, ‘STRING’, 0, ‘VALUE’)} } , ‘ZIPCODE’: {‘TYPEOFTYPES’: [‘LOCATION’],‘FUNCTIONS’: {‘ENTITYCREATION’: ‘putNetwork’} , ‘UNIQUEATTIBUTES’:[‘ZIPCODE’], ‘TOKENENTITIESRELATIONSHIPS’: [ ], ‘ATTRIBUTES’: {‘STATE’:(4, ‘STRING’, 0, ‘VALUE’), ‘POPULATION’: (3, ‘STRING’, 0, ‘VALUE’),‘ZIPCODE’: (2, ‘STRING’, 0, ‘VALUE’), ‘ISACTIVE’: (0, ‘BOOL’, 1,‘VALUE’), ‘ENTITYKEY’: (1, ‘STRING’, 0, ‘VALUE’)} } , ‘PAYMENTCARD’:{‘TYPEOFTYPES’: [‘PAYMENTCARDS’], ‘FUNCTIONS’: {‘ENTITYCREATION’:‘putNetwork’} , ‘UNIQUEATTIBUTES’: [‘CARDNUMBER’],‘TOKENENTITIESRELATIONSHIPS’: [‘USER’], ‘ATTRIBUTES’: {‘EXPDATE’: (5,‘DATETIME’, 0, ‘VALUE’), ‘ENTITYKEY’: (1, ‘STRING’, 0, ‘VALUE’),‘CARDTYPE’: (4, ‘STRING’, 0, ‘VALUE’), ‘CARDNUMBER’: (2, ‘STRING’, 0,‘VALUE’), ‘USER’: (3, ‘STRING’, 0, ‘VALUE’), ‘ISACTIVE’: (0, ‘BOOL’, 1,‘VALUE’)} } , ‘GENERICTOKEN’: {‘TYPEOFTYPES’: [‘COUPON’], ‘FUNCTIONS’:{‘ENTITYCREATION’: ‘putNetwork’} , ‘UNIQUEATTIBUTES’:[‘GENERICTOKENNAME’], ‘TOKENENTITIESRELATIONSHIPS’: [‘MERCHANT’],‘ATTRIBUTES’: {‘STATUS’: (2, ‘STRING’, 0, ‘VALUE’), ‘MERCHANT’: (3,‘STRING’, 0, ‘VALUE’), ‘TITLE’: (5, ‘STRING’, 0, ‘VALUE’), ‘NOTES’: (7,‘STRING’, 0, ‘VALUE’), ‘UPDATEDBY’: (11, ‘STRING’, 0, ‘VALUE’),‘ENTITYKEY’: (1, ‘STRING’, 0, ‘VALUE’), ‘DECRIPTION ’: (6, ‘STRING’, 0,‘VALUE’), ‘CREATEDBY’: (10, ‘STRING’, 0, ‘VALUE’), ‘LASTUPDATEDT’: (9,‘DATETIME’, 0, ‘VALUE’), ‘EXPDT ’: (13, ‘DATETIME’, 0, ‘VALUE’),‘RESTRICTIONS’: (14, ‘DICT’, 0, ‘VALUE’), ‘STARTDT’: (12, ‘DATETIME’, 0,‘VALUE’), ‘CREATIONDT’: (8, ‘DATETIME’, 0, ‘VALUE’), ‘GENERICTOKENNAME’:(4, ‘STRING’, 0, ‘VALUE’), ‘ISACTIVE’: (0, ‘BOOL’, 1, ‘VALUE’)} } ,‘USER’: {‘TYPEOFTYPES’: [‘USERS’, ‘SYNTHETICNETWORKS’], ‘FUNCTIONS’:{‘ENTITYCREATION’: ‘putNetwork’} , ‘UNIQUEATTIBUTES’: [‘USERNAME’],‘TOKENENTITIESRELATIONSHIPS’: [‘USERS’], ‘ATTRIBUTES’: {‘USERNAME’: (5,‘STRING’, 0, ‘VALUE’), ‘USERS’: (2, ‘STRING’, 0, ‘VALUE’), ‘FIRSTNAME’:(3, ‘STRING’, 0, ‘VALUE’), ‘LASTNAME’: (4, ‘STRING’, 0, ‘VALUE’),‘ENTITYKEY’: (1, ‘STRING’, 0, ‘VALUE’), ‘ISACTIVE’: (0, ‘BOOL’, 1,‘VALUE’)} } , ‘TWEETS’: {‘TYPEOFTYPES’: [‘TOKENENTITY’], ‘FUNCTIONS’:{‘ENTITYCREATION’: ‘putWGTNetwork’} , ‘UNIQUEATTIBUTES’: [‘TWEETID’],‘TOKENENTITIESRELATIONSHIPS’: [‘TWITTERUSER’], ‘ATTRIBUTES’: {‘Title’:(4, ‘STRING’, 0, ‘VALUE’), ‘RawTweet’: (5, ‘STRING’, 0, ‘VALUE’),‘DATETIME’: (3, ‘STRING’, 0, ‘VALUE’), ‘CLEANEDTWEET’: (6, ‘STRING’, 0,‘VALUE’), ‘ENTITYKEY’: (1, ‘STRING’, 0, ‘VALUE’), ‘TWEETID’: (2,‘STRING’, 0, ‘VALUE’), ‘ISACTIVE’: (0, ‘BOOL’, 1, ‘VALUE’)} } , ‘MODEL’:{‘TYPEOFTYPES’: [‘MODELS’], ‘FUNCTIONS’: {‘ENTITYCREATION’:‘putNetwork’} , ‘UNIQUEATTIBUTES’: [‘MODELNAME’],‘TOKENENTITIESRELATIONSHIPS’: [‘USER’, ‘MERCHANT’, ‘PAYMENTCARD’],‘ATTRIBUTES’: {‘XML’: (2, ‘STRING’, 0, ‘VALUE’), ‘MODELNAME’: (3,‘STRING’, 0, ‘VALUE’), ‘DESCRIPTION’: (4, ‘STRING’, 0, ‘VALUE’),‘ENTITYKEY’: (1, ‘STRING’, 0, ‘VALUE’), ‘TYPEOF ’: (5, ‘STRING’, 0,‘VALUE’), ‘ISACTIVE’: (0, ‘BOOL’, 1, ‘VALUE’)} } , ‘MCCSEG’:{‘TYPEOFTYPES’: [‘MCCSEG’], ‘FUNCTIONS’: {‘ENTITYCREATION’:‘putWGTNetwork’} , ‘UNIQUEATTIBUTES’: [‘MCCSEGID’],‘TOKENENTITIESRELATIONSHIPS’: { } , ‘ATTRIBUTES’: {‘MCCSEGID’: (2,‘STRING’, 0, ‘VALUE’), ‘MCCSEGNAME’: (3, ‘STRING’, 0, ‘VALUE’),‘ISACTIVE’: (0, ‘BOOL’, 1, ‘VALUE’), ‘ENTITYKEY’: (1, ‘STRING’, 0,‘VALUE’)} } , ‘TOKENENTITY’: {‘TYPEOFTYPES’: [‘TOKENENTITY’],‘FUNCTIONS’: {‘ENTITYCREATION’: ‘putWGTNetwork’} , ‘UNIQUEATTIBUTES’:[‘TOKENENTITYKEY’], ‘TOKENENTITIESRELATIONSHIPS’: { } , ‘ATTRIBUTES’:{‘STATUS’: (4, ‘STRING’, 0, ‘VALUE’), ‘ISSUEDDATE’: (5, ‘STRING’, 0,‘VALUE’), ‘DOUBLELINKED’: (8, ‘BOOL’, 1, ‘VALUE’), ‘BASEUUID’: (1,‘STRING’, 0, ‘VALUE’), ‘WEIGHT’: (6, ‘STRING’, 0, ‘VALUE’), ‘BASETYPE’:(3, ‘STRING’, 0, ‘VALUE’), ‘CATEGORY’: (7, ‘STRING’, 0, ‘VALUE’),‘ISACTIVE’: (0, ‘BOOL’, 1, ‘VALUE’), ‘TOKENENTITYKEY’: (2, ‘STRING’, 0,‘VALUE’)} } }

FIG. 3 shows a block diagram illustrating example EXDB componentconfigurations in some embodiments of the EXDB. In some embodiments, theEXDB may aggregate data from a variety of sources to generatecentralized personal information. The may also aggregate various typesof data in order to generate the centralized personal information. Forexample, the EXDB may utilize search results aggregation component(s)301 (e.g., such as described in FIGS. 4-5) to aggregate search resultsfrom across a wide range of computer networked systems, e.g., theInternet. As another example, the EXDB may utilize transaction dataaggregation component(s) 302 (e.g., such as described in FIGS. 6-9) toaggregate transaction data, e.g., from transaction processing procedureby a payment network. As another example, the EXDB may utilize serviceusage data aggregation component(s) 303 (e.g., such as described inFIGS. 6-9) to aggregate data on user's usage of various servicesassociated with the EXDB. As another example, the EXDB may utilizeenrollment data component(s) 304 (e.g., such as described in FIGS.12-13) to aggregate data on user's enrollment into various servicesassociated with the EXDB. As another example, the EXDB may utilize emaildata component(s) 305 a (e.g., such as described in FIG. 37) toaggregate data regarding the user's email correspondence history intovarious services associated with the EXDB. As another example, the EXDBmay utilize social data aggregation component(s) 305 (e.g., such asdescribed in FIGS. 10-11) to aggregate data on user's usage of varioussocial networking services accessible by the EXDB. In one embodiment,the aggregated data may be used to generate dictionary entries. Furtherdetail regarding the generation of dictionary entries may be foundthroughout this specification, drawings, and claims and particularlywith reference to FIG. 1 and FIG. 46.

In some embodiments, the EXDB may acquire the aggregated data, andnormalize the data into formats that are suitable for uniform storage,indexing, maintenance, and/or further processing via data recordnormalization component(s) 306 (e.g., such as described in FIGS. 14A-B).The EXDB may extract data from the normalized data records, andrecognize data fields, e.g., the EXDB may identify the attributes ofeach field of data included in the normalized data records via datafield recognition component(s) 307 (e.g., such as described in FIG. 15).For example, the EXDB may identify names, user ID(s), addresses, networkaddresses, comments and/or specific words within the comments, images,blog posts, video, content within the video, and/or the like from theaggregated data. In some embodiments, for each field of data, the EXDBmay classify entity types associated with the field of data, as well asentity identifiers associated with the field of data, e.g., viacomponent(s) 308 (e.g., such as described in FIG. 16). For example, theEXDB may identify an Internet Protocol (IP) address data field to beassociated with a user ID john.q.public (consumer entity type), a userJohn Q. Public (consumer entity type), a household (the Publichousehold—a multi-consumer entity type/household entity type), amerchant entity type with identifier Acme Merchant Store, Inc. fromwhich purchases are made from the IP address, an Issuer Bank type withidentifier First National Bank associated with the purchases made fromthe IP address, and/or the like. In some embodiments, the EXDB mayutilize the entity types and entity identifiers to correlate entitiesacross each other, e.g., via cross-entity correlation component(s) 309(e.g., such as described in FIG. 17). For example, the EXDB mayidentify, from the aggregated data, that a household entity withidentifier H123 may include a user entity with identifier John Q. Publicand social identifier john.q.public@facebook.com, a second user entitywith identifier Jane P. Doe with social identifier jpdoe@twitter.com, acomputer entity with identifier IP address 192.168.4.5, a card accountentity with identifier ****1234, a bank issuer entity with identifierAB23145, a merchant entity with identifier Acme Stores, Inc. where thehousehold sub-entities make purchases, and/or the like. In someembodiments, the EXDB may utilize the entity identifiers, dataassociated with each entity and/or correlated entities to identifyassociations to other entities, e.g., via entity attribute associationcomponent(s) 310 (e.g., such as described in FIG. 18). For example, theEXDB may identify specific purchases made via purchase transactions bymembers of the household, and thereby identify attributes of members ofthe household on the basis of the purchases in the purchase transactionsmade by members of the household. Based on such correlations andassociations, the EXDB may update a profile for each entity identifiedfrom the aggregated data, as well as a social graph interrelating theentities identified in the aggregated data, e.g., via entityprofile-graph updating component(s) 311 (e.g., such as described inFIGS. 19, 40, 41A-E and 42A-C). In some embodiments, the updating ofprofile and/or social graphs for an entity may trigger a search foradditional data that may be relevant to the newly identifiedcorrelations and associations for each entity, e.g., via search termgeneration component(s) 313-314 (e.g., such as described in FIG. 20).For example, the updating of a profile and/or social graph may triggersearches across the Internet, social networking websites, transactiondata from payment networks, services enrolled into and/or utilized bythe entities, and/or the like. In some embodiments, such updating ofentity profiles and/or social graphs may be performed continuously,periodically, on-demand, and/or the like.

FIG. 4 shows a data flow diagram illustrating an example search resultaggregation procedure in some embodiments of the EXDB. In someimplementations, the pay network server may obtain a trigger to performa search. For example, the pay network server may periodically perform asearch update of its aggregated search database, e.g., 410, with newinformation available from a variety of sources, such as the Internet.As another example, a request for on-demand search update may beobtained as a result of a user wishing to enroll in a service, for whichthe pay network server may facilitate data entry by providing anautomated web form filling system using information about the userobtained from the search update. In some implementations, the paynetwork server may parse the trigger to extract keywords using which toperform an aggregated search. The pay network server may generate aquery for application programming interface (API) templates for varioussearch engines (e.g., Google™, Bing®, AskJeeves, market data searchengines, etc.) from which to collect data for aggregation. The paynetwork server may query, e.g., 412, a pay network database, e.g., 407,for search API templates for the search engines. For example, the paynetwork server may utilize PHP/SQL commands similar to the examplesprovided above. The database may provide, e.g., 413, a list of APItemplates in response. Based on the list of API templates, the paynetwork server may generate search requests, e.g., 414. The pay networkserver may issue the generated search requests, e.g., 415 a-c, to thesearch engine servers, e.g., 401 a-c. For example, the pay networkserver may issue PHP commands to request the search engine for searchresults. An example listing of commands to issue search requests 415a-c, substantially in the form of PHP commands, is provided below:

<?PHP // API URL with access key $url =[″https://ajax.googleapis.com/ajax/services/search/web?v=1.0&″ . ″q=”$keywords “&key=1234567890987654&userip=datagraph.cpip.com″]; // SendSearch Request $ch = curl_init( ); curl_setopt($ch, CURLOPT_URL, $url);curl_setopt($ch, CURLOPT_RETURNTRANSFER, 1); curl_setopt($ch,CURLOPT_REFERER, “datagraph.cpip.com”); $body = curl_exec($ch);curl_close($ch); // Obtain, parse search results $json =json_decode($body); ?>

In some embodiments, the search engine servers may query, e.g., 417 a-c,their search databases, e.g., 402 a-c, for search results falling withinthe scope of the search keywords. In response to the search queries, thesearch databases may provide search results, e.g., 418 a-c, to thesearch engine servers. The search engine servers may return the searchresults obtained from the search databases, e.g., 419 a-c, to the paynetwork server making the search requests. An example listing of searchresults 419 a-c, substantially in the form of JavaScript Object Notation(JSON)-formatted data, is provided below:

{“responseData”: { “results”: [ { “GsearchResultClass”: “GwebSearch”,“unescapedUrl”: “http://en.wikipedia.org/wiki/John_Q_Public”, “url”:“http://en.wikipedia.org/wiki/John_Q_Public”, “visibleUrl”:“en.wikipedia.org”, “cacheUrl”:“http://www.google.com/search?q\u003dcache:TwrPfhd22hYJ:en.wikipedia.org”, “title”: “\u003cb\u003eJohn Q. Public\u003c/b\u003e -Wikipedia, the freeencyclopedia”, “titleNoFormatting”: “John Q. Public -Wikipedia, the free encyclopedia”, “content”: “\[1\] In 2006, he servedas Chief Technology Officer...” }, { “GsearchResultClass”: “GwebSearch”,“unescapedUrl”: “http://www.imdb.com/name/nm0385296/”, “url”:“http://www.imdb.com/name/nm0385296/”, “visibleUrl”: “www.imdb.com”,“cacheUrl”:“http://www.google.com/search?q\u003dcache:1i34KkqnsooJ:www.imdb. com”,“title”: “\u003cb\u003eJohn Q. Public\u003c/b\u003e”,“titleNoFormatting”: “John Q. Public”, “content”: “Self: Zoolander.Socialite \u003cb\u003eJohn Q. Public\u003c/b\u003e...” }, ... ],“cursor”: { “pages”: [ { “start”: “0”, “label”: 1 }, { “start”: “4”,“label”: 2 }, { “start”: “8”, “label”: 3 }, { “start”: “12”,“label”: 4 }], “estimatedResultCount”: “59600000”, “currentPageIndex”: 0,“moreResultsUrl”:“http://www.google.com/search?oe\u003dutf8\u0026ie\u003dutf8...” } } ,“responseDetails”: null, “responseStatus”: 200}

In some embodiments, the pay network server may store the aggregatedsearch results, e.g., 420, in an aggregated search database, e.g., 410a.

FIG. 5 shows a logic flow diagram illustrating example aspects ofaggregating search results in some embodiments of the EXDB, e.g., aSearch Results Aggregation (“SRA”) component 500. In someimplementations, the pay network server may obtain a trigger to performa search, e.g., 501. For example, the pay network server mayperiodically perform a search update of its aggregated search databasewith new information available from a variety of sources, such as theInternet. As another example, a request for on-demand search update maybe obtained as a result of a user wishing to enroll in a service, forwhich the pay network server may facilitate data entry by providing anautomated web form filling system using information about the userobtained from the search update. In some implementations, the paynetwork server may parse the trigger, e.g., 502, to extract keywordsusing which to perform an aggregated search. The pay network server maydetermine the search engines to search, e.g., 503, using the extractedkeywords. Then, the pay network server may generate a query forapplication programming interface (API) templates for the various searchengines (e.g., Google™, Bing®, AskJeeves, market data search engines,etc.) from which to collect data for aggregation, e.g., 504. The paynetwork server may query, e.g., 505, a pay network database for searchAPI templates for the search engines. For example, the pay networkserver may utilize PHP/SQL commands similar to the examples providedabove. The database may provide, e.g., 505, a list of API templates inresponse. Based on the list of API templates, the pay network server maygenerate search requests, e.g., 506. The pay network server may issuethe generated search requests to the search engine servers. The searchengine servers may parse the obtained search results(s), e.g., 507, andquery, e.g., 508, their search databases for search results fallingwithin the scope of the search keywords. In response to the searchqueries, the search databases may provide search results, e.g., 509, tothe search engine servers. The search engine servers may return thesearch results obtained from the search databases, e.g., 510, to the paynetwork server making the search requests. The pay network server maygenerate, e.g., 511, and store the aggregated search results, e.g., 512,in an aggregated search database.

FIGS. 6A-D show data flow diagrams illustrating an example card-basedtransaction execution procedure in some embodiments of the EXDB. In someimplementations, a user, e.g., 601, may desire to purchase a product,service, offering, and/or the like (“product”), from a merchant. Theuser may communicate with a merchant server, e.g., 603, via a clientsuch as, but not limited to: a personal computer, mobile device,television, point-of-sale terminal, kiosk, ATM, and/or the like (e.g.,602). For example, the user may provide user input, e.g., purchase input611, into the client indicating the user's desire to purchase theproduct. In various implementations, the user input may include, but notbe limited to: keyboard entry, card swipe, activating a RFID/NFC enabledhardware device (e.g., electronic card having multiple accounts,smartphone, tablet, etc.), mouse clicks, depressing buttons on ajoystick/game console, voice commands, single/multi-touch gestures on atouch-sensitive interface, touching user interface elements on atouch-sensitive display, and/or the like. For example, the user maydirect a browser application executing on the client device to a websiteof the merchant, and may select a product from the website via clickingon a hyperlink presented to the user via the website. As anotherexample, the client may obtain track 1 data from the user's card (e.g.,credit card, debit card, prepaid card, charge card, etc.), such as theexample track 1 data provided below:

%B123456789012345{circumflex over ( )}PUBLIC/J.Q.{circumflex over( )}99011200000000000000**901******?* (wherein ‘123456789012345’ is thecard number of ‘J.Q. Public’ and has a CVV number of 901. ‘990112’ is aservice code, and *** represents decimal digits which change randomlyeach time the card is used.)

In some implementations, the client may generate a purchase ordermessage, e.g., 612, and provide, e.g., 613, the generated purchase ordermessage to the merchant server. For example, a browser applicationexecuting on the client may provide, on behalf of the user, a (Secure)Hypertext Transfer Protocol (“HTTP(S)”) GET message including theproduct order details for the merchant server in the form of dataformatted according to the eXtensible Markup Language (“XML”). Below isan example HTTP(S) GET message including an XML-formatted purchase ordermessage for the merchant server:

GET /purchase.php HTTP/1.1 Host: www.merchant.com Content-Type:Application/XML Content-Length: 1306 <?XML version = “1.0” encoding =“UTF-8”?> <purchase_order> <order_ID>4NFU4RG94</order_ID><timestamp>2011-02-22 15:22:43</timestamp><user_ID>john.q.public@gmail.com</user_ID> <client_details><client_IP>192.168.23.126</client_IP><client_type>smartphone</client_type> <client_model>HTCHero</client_model> <OS>Android 2.2</OS><app_installed_flag>true</app_installed_flag> </client_details><purchase_details> <num_products>1</num_products> <product><product_type>book</product_type> <product_params> <product_title>XMLfor dummies</product_title> <ISBN>938-2-14-168710-0</ISBN> <edition>2nded.</edition> <cover>hardbound</cover> <seller>bestbuybooks</seller></product_params> <quantity>1</quantity> </product> </purchase_details><account_params> <account_name>John Q. Public</account_name><account_type>credit</account_type><account_num>123456789012345</account_num> <billing_address>123 GreenSt., Norman, OK 98765</billing_address> <phone>123-456-7809</phone><sign>/jqp/</sign> <confirm_type>email</confirm_type><contact_info>john.q.public@gmail.com</contact_info> </account_params><shipping_info> <shipping_adress>same as billing</shipping_address><ship_type>expedited</ship_type> <ship_carrier>FedEx</ship_carrier><ship_account>123-45-678</ship_account><tracking_flag>true</tracking_flag> <sign_flag>false</sign_flag></shipping_info> </purchase_order>

In some implementations, the merchant server may obtain the purchaseorder message from the client, and may parse the purchase order messageto extract details of the purchase order from the user. The merchantserver may generate a card query request, e.g., 614 to determine whetherthe transaction can be processed. For example, the merchant server mayattempt to determine whether the user has sufficient funds to pay forthe purchase in a card account provided with the purchase order. Themerchant server may provide the generated card query request, e.g., 615,to an acquirer server, e.g., 604. For example, the acquirer server maybe a server of an acquirer financial institution (“acquirer”)maintaining an account of the merchant. For example, the proceeds oftransactions processed by the merchant may be deposited into an accountmaintained by the acquirer. In some implementations, the card queryrequest may include details such as, but not limited to: the costs tothe user involved in the transaction, card account details of the user,user billing and/or shipping information, and/or the like. For example,the merchant server may provide a HTTP(S) POST message including anXML-formatted card query request similar to the example listing providedbelow:

POST /cardquery.php HTTP/1.1 Host: www.acquirer.com Content-Type:Application/XML Content-Length: 624 <?XML version = “1.0” encoding =“UTF-8”?> <card_query_request> <query_ID>VNEI39FK</query_ID><timestamp>2011-02-22 15:22:44</timestamp> <purchase_summary><num_products>1</num_products> <product> <product_summary>Book - XML fordummies</product_summary> <product_quantity>1</product_quantity?</product> </purchase_summary><transaction_cost>$34.78</transaction_cost> <account_params><account_name>John Q. Public</account_name><account_type>credit</account_type><account_num>123456789012345</account_num> <billing_address>123 GreenSt., Norman, OK 98765</billing_address> <phone>123-456-7809</phone><sign>/jqp/</sign> </account_params> <merchant_params><merchant_id>3FBCR4INC</merchant_id> <merchant_name>Books & Things,Inc.</merchant_name><merchant_auth_key>1NNF484MCP59CHB27365</merchant_auth_ke y></merchant_params> </card_query_request>

In some implementations, the acquirer server may generate a cardauthorization request, e.g., 616, using the obtained card query request,and provide the card authorization request, e.g., 617, to a pay networkserver, e.g., 605. For example, the acquirer server may redirect theHTTP(S) POST message in the example above from the merchant server tothe pay network server.

In some implementations, the pay network server may determine whetherthe user has enrolled in value-added user services. For example, the paynetwork server may query 618 a database, e.g., pay network database 407,for user service enrollment data. For example, the server may utilizePHP/SQL commands similar to the example provided above to query the paynetwork database. In some implementations, the database may provide theuser service enrollment data, e.g., 619. The user enrollment data mayinclude a flag indicating whether the user is enrolled or not, as wellas instructions, data, login URL, login API call template and/or thelike for facilitating access of the user-enrolled services. For example,in some implementations, the pay network server may redirect the clientto a value-add server (e.g., such as a social network server where thevalue-add service is related to social networking) by providing aHTTP(S) REDIRECT 300 message, similar to the example below:

HTTP/1.1 300 Multiple Choices Location:https://www.facebook.com/dialog/oauth?client_id=snpa_app_ID&redirect_uri=www.paynetwork.com/purchase.php <html> <head><title>300 MultipleChoices</title></head> <body><h1>Multiple Choices</h1></body> </html>

In some implementations, the pay network server may provide paymentinformation extracted from the card authorization request to thevalue-add server as part of a value add service request, e.g., 620. Forexample, the pay network server may provide a HTTP(S) POST message tothe value-add server, similar to the example below:

POST /valueservices.php HTTP/1.1 Host: www.valueadd.com Content-Type:Application/XML Content-Length: 1306 <?XML version = “1.0” encoding =“UTF-8”?> <service_request> <request_ID>4NFU4RG94</order_ID><timestamp>2011-02-22 15:22:43</timestamp><user_ID>john.q.public@gmail.com</user_ID> <client_details><client_IP>192.168.23.126</client_IP><client_type>smartphone</client_type> <client_model>HTCHero</client_model> <OS>Android 2.2</OS><app_installed_flag>true</app_installed_flag> </client_details><account_params> <account_name>John Q. Public</account_name><account_type>credit</account_type><account_num>123456789012345</account_num> <billing_address>123 GreenSt., Norman, OK 98765</billing_address> <phone>123-456-7809</phone><sign>/jqp/</sign> <confirm_type>email</confirm_type><contact_info>john.q.public@gmail.com</contact_info> </account_params><!--optional--> <merchant> <merchant_id>CQN3Y42N</merchant_id><merchant_name>Acme Tech, Inc.</merchant_name><user_name>john.q.public</user_name> <cardlist>www.acme.com/user/john.q.public/cclist.xml<cardlist><user_account_preference>1 3 2 4 7 6 5<user_account_preference></merchant> </service_request>

In some implementations, the value-add server may provide a serviceinput request, e.g., 621, to the client. For example, the value-addserver may provide a HTML input/login form to the client. The client maydisplay, e.g., 622, the login form for the user. In someimplementations, the user may provide login input into the client, e.g.,623, and the client may generate a service input response, e.g., 624,for the value-add server. In some implementations, the value-add servermay provide value-add services according to user value-add serviceenrollment data, user profile, etc., stored on the value-add server, andbased on the user service input. Based on the provision of value-addservices, the value-add server may generate a value-add serviceresponse, e.g., 626, and provide the response to the pay network server.For example, the value-add server may provide a HTTP(S) POST messagesimilar to the example below:

POST /serviceresponse.php HTTP/1.1 Host: www.paynet.com Content-Type:Application/XML Content-Length: 1306 <?XML version = “1.0” encoding =“UTF-8”?> <service_response> <request_ID>4NFU4RG94</order_ID><timestamp>2011-02-22 15:22:43</timestamp> <result>serviced</result><servcode>943528976302-45569-003829-04</servcode> </service_response>

In some implementations, upon receiving the value-add service responsefrom the value-add server, the pay network server may extract theenrollment service data from the response for addition to a transactiondata record. In some implementations, the pay network server may forwardthe card authorization request to an appropriate pay network server,e.g., 628, which may parse the card authorization request to extractdetails of the request. Using the extracted fields and field values, thepay network server may generate a query, e.g., 629, for an issuer servercorresponding to the user's card account. For example, the user's cardaccount, the details of which the user may have provided via theclient-generated purchase order message, may be linked to an issuerfinancial institution (“issuer”), such as a banking institution, whichissued the card account for the user. An issuer server, e.g., 608 a-n,of the issuer may maintain details of the user's card account. In someimplementations, a database, e.g., pay network database 607, may storedetails of the issuer servers and card account numbers associated withthe issuer servers. For example, the database may be a relationaldatabase responsive to Structured Query Language (“SQL”) commands. Thepay network server may execute a hypertext preprocessor (“PHP”) scriptincluding SQL commands to query the database for details of the issuerserver. An example PHP/SQL command listing, illustrating substantiveaspects of querying the database, is provided below:

<?PHP header(′Content-Type: text/plain′);mysql_connect(“254.93.179.112”,$DBserver,$password); // access databaseserver mysql_select_db(“ISSUERS.SQL”); // select database table tosearch //create query for issuer server data $query = “SELECTissuer_name issuer_address issuer_id ip_address mac_address auth_keyport_num security_settings_list FROM IssuerTable WHERE account_num LIKE′%′ $accountnum”; $result = mysql_query($query); // perform the searchquery mysql_close(“ISSUERS.SQL”); // close database access ?>

In response to obtaining the issuer server query, e.g., 629, the paynetwork database may provide, e.g., 630, the requested issuer serverdata to the pay network server. In some implementations, the pay networkserver may utilize the issuer server data to generate a forwarding cardauthorization request, e.g., 631, to redirect the card authorizationrequest from the acquirer server to the issuer server. The pay networkserver may provide the card authorization request, e.g., 632, to theissuer server. In some implementations, the issuer server, e.g., 608,may parse the card authorization request, and based on the requestdetails may query 633 a database, e.g., user profile database 609, fordata of the user's card account. For example, the issuer server mayissue PHP/SQL commands similar to the example provided below:

<?PHP header(′Content-Type: text/plain′);mysql_connect(“254.93.179.112”,$DBserver,$password); // access databaseserver mysql_select_db(“USERS.SQL”); // select database table to search//create query for user data $query = “SELECT user_id user_nameuser_balance account_type FROM UserTable WHERE account_num LIKE ′%′$accountnum”; $result = mysql_query($query); // perform the search querymysql_close(“USERS.SQL“); // close database access ?>

In some implementations, on obtaining the user data, e.g., 634, theissuer server may determine whether the user can pay for the transactionusing funds available in the account, e.g., 635. For example, the issuerserver may determine whether the user has a sufficient balance remainingin the account, sufficient credit associated with the account, and/orthe like. If the issuer server determines that the user can pay for thetransaction using the funds available in the account, the server mayprovide an authorization message, e.g., 636, to the pay network server.For example, the server may provide a HTTP(S) POST message similar tothe examples above.

In some implementations, the pay network server may obtain theauthorization message, and parse the message to extract authorizationdetails. Upon determining that the user possesses sufficient funds forthe transaction, the pay network server may generate a transaction datarecord from the card authorization request it received, and store, e.g.,639, the details of the transaction and authorization relating to thetransaction in a database, e.g., pay network database 607. For example,the pay network server may issue PHP/SQL commands similar to the examplelisting below to store the transaction data in a database:

<?PHP header(′Content-Type: text/plain′);mysql_connect(″254.92.185.103”,$DBserver,$password); // access databaseserver mysql_select(″TRANSACTIONS.SQL″); // select database to appendmysql_query(“INSERT INTO PurchasesTable (timestamp,purchase_summary_list, num_products, product_summary, product_quantity,transaction_cost, account_params_list, account_name, account_type,account_num, billing_addres, zipcode, phone, sign, merchant_params_list,merchant_id, merchant_name, merchant_auth_key) VALUES (time( ),$purchase_summary_list, $num_products, $product_summary,$product_quantity, $transaction_cost, $account_params_list,$account_name, $account_type, $account_num, $billing_addres, $zipcode,$phone, $sign, $merchant_params_list, $merchant_id, $merchant_name,$merchant_auth_key)”); // add data to table in databasemysql_close(″TRANSACTIONS.SQL″); // close connection to database ?>

In some implementations, the pay network server may forward theauthorization message, e.g., 640, to the acquirer server, which may inturn forward the authorization message, e.g., 640, to the merchantserver. The merchant may obtain the authorization message, and determinefrom it that the user possesses sufficient funds in the card account toconduct the transaction. The merchant server may add a record of thetransaction for the user to a batch of transaction data relating toauthorized transactions. For example, the merchant may append the XMLdata pertaining to the user transaction to an XML data file comprisingXML data for transactions that have been authorized for various users,e.g., 641, and store the XML data file, e.g., 642, in a database, e.g.,merchant database 604. For example, a batch XML data file may bestructured similar to the example XML data structure template providedbelow:

<?XML version = “1.0” encoding = “UTF-8”?> <merchant_data><merchant_id>3FBCR4INC</merchant_id> <merchant_name>Books & Things,Inc.</merchant_name><merchant_auth_key>1NNF484MCP59CHB27365</merchant_auth_ke y><account_number>123456789</account_number> </merchant_data><transaction_data> <transaction 1> ... </transaction 1> <transaction 2>... </transaction 2> . . . <transaction n> ... </transaction n></transaction_data>

In some implementations, the server may also generate a purchasereceipt, e.g., 643, and provide the purchase receipt to the client. Theclient may render and display, e.g., 644, the purchase receipt for theuser. For example, the client may render a webpage, electronic message,text/SMS message, buffer a voicemail, emit a ring tone, and/or play anaudio message, etc., and provide output including, but not limited to:sounds, music, audio, video, images, tactile feedback, vibration alerts(e.g., on vibration-capable client devices such as a smartphone etc.),and/or the like.

With reference to FIG. 6C, in some implementations, the merchant servermay initiate clearance of a batch of authorized transactions. Forexample, the merchant server may generate a batch data request, e.g.,645, and provide the request, e.g., 646, to a database, e.g., merchantdatabase 604. For example, the merchant server may utilize PHP/SQLcommands similar to the examples provided above to query a relationaldatabase. In response to the batch data request, the database mayprovide the requested batch data, e.g., 647. The server may generate abatch clearance request, e.g., 648, using the batch data obtained fromthe database, and provide, e.g., 641, the batch clearance request to anacquirer server, e.g., 610. For example, the merchant server may providea HTTP(S) POST message including XML-formatted batch data in the messagebody for the acquirer server. The acquirer server may generate, e.g.,650, a batch payment request using the obtained batch clearance request,and provide the batch payment request to the pay network server, e.g.,651. The pay network server may parse the batch payment request, andextract the transaction data for each transaction stored in the batchpayment request, e.g., 652. The pay network server may store thetransaction data, e.g., 653, for each transaction in a database, e.g.,pay network database 607. For each extracted transaction, the paynetwork server may query, e.g., 654-655, a database, e.g., pay networkdatabase 607, for an address of an issuer server. For example, the paynetwork server may utilize PHP/SQL commands similar to the examplesprovided above. The pay network server may generate an individualpayment request, e.g., 656, for each transaction for which it hasextracted transaction data, and provide the individual payment request,e.g., 657, to the issuer server, e.g., 608. For example, the pay networkserver may provide a HTTP(S) POST request similar to the example below:

POST /requestpay.php HTTP/1.1 Host: www.issuer.com Content-Type:Application/XML Content-Length: 788 <?XML version = “1.0” encoding =“UTF-8”?> <pay_request> <request_ID>CNI4ICNW2</request_ID><timestamp>2011-02-22 17:00:01</timestamp><pay_amount>$34.78</pay_amount> <account_params> <account_name>John Q.Public</account_name> <account_type>credit</account_type><account_num>123456789012345</account_num> <billing_address>123 GreenSt., Norman, OK 98765</billing_address> <phone>123-456-7809</phone><sign>/jqp/</sign> </account_params> <merchant_params><merchant_id>3FBCR4INC</merchant_id> <merchant_name>Books & Things,Inc.</merchant_name><merchant_auth_key>1NNF484MCP59CHB27365</merchant_auth_ke y></merchant_params> <purchase_summary> <num_products>1</num_products><product> <product_summary>Book - XML for dummies</product_summary><product_quantity>1</product_quantity? </product> </purchase_summary></pay_request>

In some implementations, the issuer server may generate a paymentcommand, e.g., 658. For example, the issuer server may issue a commandto deduct funds from the user's account (or add a charge to the user'scredit card account). The issuer server may issue a payment command,e.g., 659, to a database storing the user's account information, e.g.,user profile database 608. The issuer server may provide a fundstransfer message, e.g., 660, to the pay network server, which mayforward, e.g., 661, the funds transfer message to the acquirer server.An example HTTP(S) POST funds transfer message is provided below:

POST /clearance.php HTTP/1.1 Host: www.acquirer.com Content-Type:Application/XML Content-Length: 206 <?XML version = “1.0” encoding =“UTF-8”?> <deposit_ack> <request_ID>CNI4ICNW2</request_ID><clear_flag>true</clear_flag> <timestamp>2011-02-22 17:00:02</timestamp><deposit_amount>$34.78</deposit_amount> </deposit_ack>

In some implementations, the acquirer server may parse the fundstransfer message, and correlate the transaction (e.g., using therequest_ID field in the example above) to the merchant. The acquirerserver may then transfer the funds specified in the funds transfermessage to an account of the merchant, e.g., 662.

FIGS. 7A-E show logic flow diagrams illustrating example aspects ofcard-based transaction execution, resulting in generation of card-basedtransaction data and service usage data, in some embodiments of theEXDB, e.g., a Card-Based Transaction Execution (“CTE”) component 700. Insome implementations, a user may provide user input, e.g., 701, into aclient indicating the user's desire to purchase a product from amerchant. The client may generate a purchase order message, e.g., 702,and provide the generated purchase order message to the merchant server.In some implementations, the merchant server may obtain, e.g., 703, thepurchase order message from the client, and may parse the purchase ordermessage to extract details of the purchase order from the user. Exampleparsers that the merchant client may utilize are discussed further belowwith reference to FIG. 49. The merchant may generate a product dataquery, e.g., 704, for a merchant database, which may in response providethe requested product data, e.g., 705. The merchant server may generatea card query request using the product data, e.g., 704, to determinewhether the transaction can be processed. For example, the merchantserver may process the transaction only if the user has sufficient fundsto pay for the purchase in a card account provided with the purchaseorder. The merchant server may optionally provide the generated cardquery request to an acquirer server. The acquirer server may generate acard authorization request using the obtained card query request, andprovide the card authorization request to a pay network server.

In some implementations, the pay network server may determine whetherthe user has enrolled in value-added user services. For example, the paynetwork server may query a database, e.g., 707, for user serviceenrollment data. For example, the server may utilize PHP/SQL commandssimilar to the example provided above to query the pay network database.In some implementations, the database may provide the user serviceenrollment data, e.g., 708. The user enrollment data may include a flagindicating whether the user is enrolled or not, as well as instructions,data, login URL, login API call template and/or the like forfacilitating access of the user-enrolled services. For example, in someimplementations, the pay network server may redirect the client to avalue-add server (e.g., such as a social network server where thevalue-add service is related to social networking) by providing aHTTP(S) REDIRECT 300 message. In some implementations, the pay networkserver may provide payment information extracted from the cardauthorization request to the value-add server as part of a value addservice request, e.g., 710.

In some implementations, the value-add server may provide a serviceinput request, e.g., 711, to the client. The client may display, e.g.,712, the input request for the user. In some implementations, the usermay provide input into the client, e.g., 713, and the client maygenerate a service input response for the value-add server. In someimplementations, the value-add server may provide value-add servicesaccording to user value-add service enrollment data, user profile, etc.,stored on the value-add server, and based on the user service input.Based on the provision of value-add services, the value-add server maygenerate a value-add service response, e.g., 717, and provide theresponse to the pay network server. In some implementations, uponreceiving the value-add service response from the value-add server, thepay network server may extract the enrollment service data from theresponse for addition to a transaction data record, e.g., 719-720.

With reference to FIG. 7B, in some implementations, the pay networkserver may obtain the card authorization request from the acquirerserver, and may parse the card authorization request to extract detailsof the request, e.g., 720. Using the extracted fields and field values,the pay network server may generate a query, e.g., 721-722, for anissuer server corresponding to the user's card account. In response toobtaining the issuer server query the pay network database may provide,e.g., 722, the requested issuer server data to the pay network server.In some implementations, the pay network server may utilize the issuerserver data to generate a forwarding card authorization request, e.g.,723, to redirect the card authorization request from the acquirer serverto the issuer server. The pay network server may provide the cardauthorization request to the issuer server. In some implementations, theissuer server may parse, e.g., 724, the card authorization request, andbased on the request details may query a database, e.g., 725, for dataof the user's card account. In response, the database may provide therequested user data. On obtaining the user data, the issuer server maydetermine whether the user can pay for the transaction using fundsavailable in the account, e.g., 726. For example, the issuer server maydetermine whether the user has a sufficient balance remaining in theaccount, sufficient credit associated with the account, and/or the like,but comparing the data from the database with the transaction costobtained from the card authorization request. If the issuer serverdetermines that the user can pay for the transaction using the fundsavailable in the account, the server may provide an authorizationmessage, e.g., 727, to the pay network server.

In some implementations, the pay network server may obtain theauthorization message, and parse the message to extract authorizationdetails. Upon determining that the user possesses sufficient funds forthe transaction (e.g., 730, option “Yes”), the pay network server mayextract the transaction card from the authorization message and/or cardauthorization request, e.g., 733, and generate a transaction data recordusing the card transaction details. The pay network server may providethe transaction data record for storage, e.g., 734, to a database. Insome implementations, the pay network server may forward theauthorization message, e.g., 735, to the acquirer server, which may inturn forward the authorization message, e.g., 736, to the merchantserver. The merchant may obtain the authorization message, and parse theauthorization message o extract its contents, e.g., 737. The merchantserver may determine whether the user possesses sufficient funds in thecard account to conduct the transaction. If the merchant serverdetermines that the user possess sufficient funds, e.g., 738, option“Yes,” the merchant server may add the record of the transaction for theuser to a batch of transaction data relating to authorized transactions,e.g., 739-740. The merchant server may also generate a purchase receipt,e.g., 741, for the user. If the merchant server determines that the userdoes not possess sufficient funds, e.g., 738, option “No,” the merchantserver may generate an “authorization fail” message, e.g., 742. Themerchant server may provide the purchase receipt or the “authorizationfail” message to the client. The client may render and display, e.g.,743, the purchase receipt for the user.

In some implementations, the merchant server may initiate clearance of abatch of authorized transactions by generating a batch data request,e.g., 744, and providing the request to a database. In response to thebatch data request, the database may provide the requested batch data,e.g., 745, to the merchant server. The server may generate a batchclearance request, e.g., 746, using the batch data obtained from thedatabase, and provide the batch clearance request to an acquirer server.The acquirer server may generate, e.g., 748, a batch payment requestusing the obtained batch clearance request, and provide the batchpayment request to a pay network server. The pay network server mayparse, e.g., 749, the batch payment request, select a transaction storedwithin the batch data, e.g., 750, and extract the transaction data forthe transaction stored in the batch payment request, e.g., 751. The paynetwork server may generate a transaction data record, e.g., 752, andstore the transaction data, e.g., 753, the transaction in a database.For the extracted transaction, the pay network server may generate anissuer server query, e.g., 754, for an address of an issuer servermaintaining the account of the user requesting the transaction. The paynetwork server may provide the query to a database. In response, thedatabase may provide the issuer server data requested by the pay networkserver, e.g., 755. The pay network server may generate an individualpayment request, e.g., 756, for the transaction for which it hasextracted transaction data, and provide the individual payment requestto the issuer server using the issuer server data from the database.

In some implementations, the issuer server may obtain the individualpayment request, and parse, e.g., 757, the individual payment request toextract details of the request. Based on the extracted data, the issuerserver may generate a payment command, e.g., 758. For example, theissuer server may issue a command to deduct funds from the user'saccount (or add a charge to the user's credit card account). The issuerserver may issue a payment command, e.g., 759, to a database storing theuser's account information. In response, the database may update a datarecord corresponding to the user's account to reflect the debit/chargemade to the user's account. The issuer server may provide a fundstransfer message, e.g., 760, to the pay network server after the paymentcommand has been executed by the database.

In some implementations, the pay network server may check whether thereare additional transactions in the batch that need to be cleared andfunded. If there are additional transactions, e.g., 761, option “Yes,”the pay network server may process each transaction according to theprocedure described above. The pay network server may generate, e.g.,762, an aggregated funds transfer message reflecting transfer of alltransactions in the batch, and provide, e.g., 763, the funds transfermessage to the acquirer server. The acquirer server may, in response,transfer the funds specified in the funds transfer message to an accountof the merchant, e.g., 764.

FIG. 8 shows a data flow diagram illustrating an example procedure toaggregate card-based transaction data in some embodiments of the EXDB.In some implementations, the pay network server may determine a scope ofdata aggregation required to perform the analysis, e.g., 811. The paynetwork server may initiate data aggregation based on the determinedscope. The pay network server may generate a query for addresses ofserver storing transaction data within the determined scope. The paynetwork server may query, e.g., 812, a pay network database, e.g., 807a, for addresses of pay network servers that may have stored transactiondata within the determined scope of the data aggregation. For example,the pay network server may utilize PHP/SQL commands similar to theexamples provided above. The database may provide, e.g., 813, a list ofserver addresses in response to the pay network server's query. Based onthe list of server addresses, the pay network server may generatetransaction data requests, e.g., 814. The pay network server may issuethe generated transaction data requests, e.g., 815 a-c, to the other paynetwork servers, e.g., 805 b-d. The other pay network servers may query,e.g., 817 a-c, their pay network database, e.g., 807 a-d, fortransaction data falling within the scope of the transaction datarequests. In response to the transaction data queries, the pay networkdatabases may provide transaction data, e.g., 818 a-c, to the other paynetwork servers. The other pay network servers may return thetransaction data obtained from the pay network databases, e.g., 819 a-c,to the pay network server making the transaction data requests, e.g.,805 a. The pay network server, e.g., 805 a, may store the aggregatedtransaction data, e.g., 820, in an aggregated transactions database,e.g., 810 a.

FIG. 9 shows a logic flow diagram illustrating example aspects ofaggregating card-based transaction data in some embodiments of the EXDB,e.g., a Transaction Data Aggregation (“TDA”) component 900. In someimplementations, a pay network server may obtain a trigger to aggregatetransaction data, e.g., 901. For example, the server may be configuredto initiate transaction data aggregation on a regular, periodic, basis(e.g., hourly, daily, weekly, monthly, quarterly, semi-annually,annually, etc.). As another example, the server may be configured toinitiate transaction data aggregation on obtaining information that theU.S. Government (e.g., Department of Commerce, Office of Management andBudget, etc) has released new statistical data related to the U.S.business economy. As another example, the server may be configured toinitiate transaction data aggregation on-demand, upon obtaining a userinvestment strategy analysis request for processing. The pay networkserver may determine a scope of data aggregation required to perform theanalysis, e.g., 902. For example, the scope of data aggregation may bepre-determined. As another example, the scope of data aggregation may bedetermined based on a received user investment strategy analysisrequest. The pay network server may initiate data aggregation based onthe determined scope. The pay network server may generate a query foraddresses of server storing transaction data within the determinedscope, e.g., 903. The pay network server may query a database foraddresses of pay network servers that may have stored transaction datawithin the determined scope of the data aggregation. The database mayprovide, e.g., 904, a list of server addresses in response to the paynetwork server's query. Based on the list of server addresses, the paynetwork server may generate transaction data requests, e.g., 905. Thepay network server may issue the generated transaction data requests tothe other pay network servers. The other pay network servers may obtainand parse the transaction data requests, e.g., 906. Based on parsing thedata requests, the other pay network servers may generate transactiondata queries, e.g., 907, and provide the transaction data queries totheir pay network databases. In response to the transaction dataqueries, the pay network databases may provide transaction data, e.g.,908, to the other pay network servers. The other pay network servers mayreturn, e.g., 909, the transaction data obtained from the pay networkdatabases to the pay network server making the transaction datarequests. The pay network server may generate aggregated transactiondata records from the transaction data received from the other paynetwork servers, e.g., 910, and store the aggregated transaction data ina database, e.g., 911.

FIG. 10 shows a data flow diagram illustrating an example social dataaggregation procedure in some embodiments of the EXDB. In someimplementations, the pay network server may obtain a trigger to performa social data search. For example, the pay network server mayperiodically perform an update of its aggregated social database, e.g.,1010, with new information available from a variety of sources, such asthe social networking services operating on the Internet. As anotherexample, a request for on-demand social data update may be obtained as aresult of a user wishing to enroll in a service, for which the paynetwork server may facilitate data entry by providing an automated webform filling system using information about the user obtained from thesocial data update. In some implementations, the pay network server mayparse the trigger to extract keywords using which to perform anaggregated social data update. The pay network server may generate aquery for application programming interface (API) templates for varioussocial networking services (e.g., Facebook®, Twitter™, etc.) from whichto collect social data for aggregation. The pay network server mayquery, e.g., 1012, a pay network database, e.g., 1007, for socialnetwork API templates for the social networking services. For example,the pay network server may utilize PHP/SQL commands similar to theexamples provided above. The database may provide, e.g., 1013, a list ofAPI templates in response. Based on the list of API templates, the paynetwork server may generate social data requests, e.g., 1014. The paynetwork server may issue the generated social data requests, e.g., 1015a-c, to the social network servers, e.g., 1001 a-c. For example, the paynetwork server may issue PHP commands to request the social networkservers for social data. An example listing of commands to issue socialdata requests 1015 a-c, substantially in the form of PHP commands, isprovided below:

<?PHP header(‘Content-Type: text/plain’); // Obtain user ID(s) offriends of the logged-in user $friends =json_decode(file_get_contents(′https://graph.facebook.com/me/friends?access token=′$cookie[′oauth_access_token′]), true); $friend_ids =array_keys($friends); // Obtain message feed associated with the profileof the logged- in user $feed =json_decode(file_get_contents(‘https:llgraph.facebook.com/me/feed?access_token=′$cookie[′oauth_access_token′]), true); // Obtain messagesby the user's friends $result = mysql_query(′SELECT * FROM content WHEREuid IN (′ .implode($friend_ids, ′,′) . ′)′); $friend_content = array( );while ($row = mysql_fetch_assoc($result)) $friend_content [ ] $row; ?>

In some embodiments, the social network servers may query, e.g., 1017a-c, their databases, e.g., 1002 a-c, for social data results fallingwithin the scope of the social keywords. In response to the queries, thedatabases may provide social data, e.g., 1018 a-c, to the search engineservers. The social network servers may return the social data obtainedfrom the databases, e.g., 1019 a-c, to the pay network server making thesocial data requests. An example listing of social data 1019 a-c,substantially in the form of JavaScript Object Notation (JSON)-formatteddata, is provided below:

[ “data”: [ { “name”: “Tabatha Orloff”, “id”: “483722”}, { “name”:“Darren Kinnaman”, “id”: “86S743”}, { “name”: “Sharron Jutras”, “id”:“O91274”} ] }

In some embodiments, the pay network server may store the aggregatedsearch results, e.g., 1020, in an aggregated search database, e.g., 1010a.

FIG. 11 shows a logic flow diagram illustrating example aspects ofaggregating social data in some embodiments of the EXDB, e.g., a SocialData Aggregation (“SDA”) component 1100. In some implementations, thepay network server may obtain a trigger to perform a social search,e.g., 1101. For example, the pay network server may periodically performan update of its aggregated social database with new informationavailable from a variety of sources, such as the Internet. As anotherexample, a request for on-demand social data update may be obtained as aresult of a user wishing to enroll in a service, for which the paynetwork server may facilitate data entry by providing an automated webform filling system using information about the user obtained from thesocial data update. In some implementations, the pay network server mayparse the trigger, e.g., 1102, to extract keywords and/or user ID(s)using which to perform an aggregated search for social data. The paynetwork server may determine the social networking services to search,e.g., 1103, using the extracted keywords and/or user ID(s). Then, thepay network server may generate a query for application programminginterface (API) templates for the various social networking services(e.g., Facebook®, Twitter™, etc.) from which to collect social data foraggregation, e.g., 1104. The pay network server may query, e.g., 1105, apay network database for search API templates for the social networkingservices. For example, the pay network server may utilize PHP/SQLcommands similar to the examples provided above. The database mayprovide, e.g., 1105, a list of API templates in response. Based on thelist of API templates, the pay network server may generate social datarequests, e.g., 1106. The pay network server may issue the generatedsocial data requests to the social networking services. The socialnetwork servers may parse the obtained search results(s), e.g., 1107,and query, e.g., 1108, their databases for social data falling withinthe scope of the search keywords. In response to the social dataqueries, the databases may provide social data, e.g., 1109, to thesocial networking servers. The social networking servers may return thesocial data obtained from the databases, e.g., 1110, to the pay networkserver making the social data requests. The pay network server maygenerate, e.g., 1111, and store the aggregated social data, e.g., 1112,in an aggregated social database.

FIG. 12 shows a data flow diagram illustrating an example procedure forenrollment in value-add services in some embodiments of the EXDB. Insome implementations, a user, e.g., 1201, may desire to enroll in avalue-added service. Let us consider an example wherein the user desiresto enroll in social network authenticated purchase payment as avalue-added service. It is to be understood that any other value-addedservice may take the place of the below-described value-added service.The user may communicate with a pay network server, e.g., 1203, via aclient such as, but not limited to: a personal computer, mobile device,television, point-of-sale terminal, kiosk, ATM, and/or the like (e.g.,1202). For example, the user may provide user input, e.g., enroll input1211, into the client indicating the user's desire to enroll in socialnetwork authenticated purchase payment. In various implementations, theuser input may include, but not be limited to: a single tap (e.g., aone-tap mobile app purchasing embodiment) of a touchscreen interface,keyboard entry, card swipe, activating a RFID/NFC enabled hardwaredevice (e.g., electronic card having multiple accounts, smartphone,tablet, etc.) within the user device, mouse clicks, depressing buttonson a joystick/game console, voice commands, single/multi-touch gestureson a touch-sensitive interface, touching user interface elements on atouch-sensitive display, and/or the like. For example, the user mayswipe a payment card at the client 1202. In some implementations, theclient may obtain track 1 data from the user's card as enroll input 1211(e.g., credit card, debit card, prepaid card, charge card, etc.), suchas the example track 1 data provided below:

%B123456789012345{circumflex over ( )}PUBLIC/J.Q.{circumflex over( )}99011200000000000000**901******?* (wherein ‘123456789012345’ is thecard number of ‘J.Q. Public’ and has a CVV number of 901. ‘990112’ is aservice code, and *** represents decimal digits which change randomlyeach time the card is used.)

In some implementations, using the user's input, the client may generatean enrollment request, e.g., 1212, and provide the enrollment request,e.g., 1213, to the pay network server. For example, the client mayprovide a (Secure) Hypertext Transfer Protocol (“HTTP(S)”) POST messageincluding data formatted according to the eXtensible Markup Language(“XML”). Below is an example HTTP(S) POST message including anXML-formatted enrollment request for the pay network server:

POST /enroll.php HTTP/1.1 Host: www.merchant.com Content-Type:Application/XML Content-Length: 718 <?XML version = “1.0” encoding =“UTF-8”?> <enrollment_request> <cart_ID>4NFU4RG94</order_ID><timestamp>2011-02-22 15:22:43</timestamp><user_ID>john.q.public@gmail.com</user_ID> <client_details><client_IP>192.168.23.126</client_IP><client_type>smartphone</client_type> <client_model>HTCHero</client_model> <OS>Android 2.2</OS><app_installed_flag>true</app_installed_flag> </client_details><!--account_params> <optional> <account_name>John Q.Public</account_name> <account_type>credit</account_type><account_num>123456789012345</account_num> <billing_address>123 GreenSt., Norman, OK 98765</billing_address> <phone>123-456-7809</phone><sign>/jqp/</sign> <confirm_type>email</confirm_type><contact_info>john.q.public@gmail.com</contact_info> </account_params--><checkout_purchase_details> <num_products>1</num_products> <product><product_type>book</product_type> <product_params> <product_title>XMLfor dummies</product_title> <ISBN>938-2-14-168710-0</ISBN> <edition>2nded.</edition> <cover>hardbound</cover> <seller>bestbuybooks</seller></product_params> <quantity>1</quantity> </product></checkout_purchase_details> </enrollment_request>

In some implementations, the pay network server may obtain theenrollment request from the client, and extract the user's paymentdetail (e.g., XML data) from the enrollment request. For example, thepay network server may utilize a parser such as the example parsersdescribed below in the discussion with reference to FIG. 49. In someimplementations, the pay network server may query, e.g., 1214, a paynetwork database, e.g., 1204, to obtain a social network requesttemplate, e.g., 1215, to process the enrollment request. The socialnetwork request template may include instructions, data, login URL,login API call template and/or the like for facilitating social networkauthentication. For example, the database may be a relational databaseresponsive to Structured Query Language (“SQL”) commands. The merchantserver may execute a hypertext preprocessor (“PHP”) script including SQLcommands to query the database for product data. An example PHP/SQLcommand listing, illustrating substantive aspects of querying thedatabase, e.g., 1214-1215, is provided below:

<?PHP header(′Content-Type: text/plain′);mysql_connect(“254.93.179.112”,$DBserver,$password); // access databaseserver mysql_select_db(“SOCIALAUTH.SQL”); // select database table tosearch //create query $query = “SELECT template FROM EnrollTable WHEREnetwork LIKE ′%′ $socialnet”; $result = mysql_query($query); // performthe search query mysql_close(“SOCIALAUTH.SQL”); // close database access?>

In some implementations, the pay network server may redirect the clientto a social network server by providing a HTTP(S) REDIRECT 300 message,similar to the example below:

HTTP/1.1 300 Multiple Choices Location:https://www.facebook.com/dialog/oauth?client_id=snpa_app_ID&redirect_uri=www.paynetwork.com/enroll.php <html> <head><title>300 MultipleChoices</title></head> <body><h1>Multiple Choices</h1></body> </html>

In some implementations, the pay network server may provide paymentinformation extracted from the card authorization request to the socialnetwork server as part of a social network authentication enrollmentrequest, e.g., 1217. For example, the pay network server may provide aHTTP(S) POST message to the social network server, similar to theexample below:

POST /authenticate_enroll.php HTTP/1.1 Host: www.socialnet.comContent-Type: Application/XML Content-Length: 1306 <?XML version = “1.0”encoding = “UTF-8”?> <authenticate_enrollment_request><request_ID>4NFU4RG94</order_ID> <timestamp>2011-02-2215:22:43</timestamp> <user_ID>john.q.public@gmail.com</user_ID><client_details> <client_IP>192.168.23.126</client_IP><client_type>smartphone</client_type> <client_model>HTCHero</client_model> <OS>Android 2.2</OS><app_installed_flag>true</app_installed_flag> </client_details><account_params> <account_name>John Q. Public</account_name><account_type>credit</account_type><account_num>123456789012345</account_num> <billing_address>123 GreenSt., Norman, OK 98765</billing_address> <phone>123-456-7809</phone><sign>/jqp/</sign> <confirm_type>email</confirm_type><contact_info>john.q.public@gmail.com</contact_info> </account_params></authenticate_enrollment_request>

In some implementations, the social network server may provide a socialnetwork login request, e.g., 1218, to the client. For example, thesocial network server may provide a HTML input form to the client. Theclient may display, e.g., 1219, the login form for the user. In someimplementations, the user may provide login input into the client, e.g.,1220, and the client may generate a social network login response, e.g.,1221, for the social network server. In some implementations, the socialnetwork server may authenticate the login credentials of the user, andaccess payment account information of the user stored within the socialnetwork, e.g., in a social network database. Upon authentication, thesocial network server may generate an authentication data record for theuser, e.g., 1222, and provide an enrollment notification, e.g., 1224, tothe pay network server. For example, the social network server mayprovide a HTTP(S) POST message similar to the example below:

POST /enrollnotification.php HTTP/1.1 Host: www.paynet.com Content-Type:Application/XML Content-Length: 1306 <?XML version = “1.0” encoding =“UTF-8”?> <enroll_notification> <request_ID>4NFU4RG94</order_ID><timestamp>2011-02-22 15:22:43</timestamp> <result>enrolled</result></enroll_notification>

Upon receiving notification of enrollment from the social networkserver, the pay network server may generate, e.g., 1225, a userenrollment data record, and store the enrollment data record in a paynetwork database, e.g., 1226, to complete enrollment. In someimplementations, the enrollment data record may include the informationfrom the enrollment notification 1224.

FIG. 13 shows a logic flow diagram illustrating example aspects ofenrollment in a value-added service in some embodiments of the EXDB,e.g., a Value-Add Service Enrollment (“VASE”) component 1300. In someimplementations, a user, e.g., 1201, may desire to enroll in avalue-added service. Let us consider an example wherein the user desiresto enroll in social network authenticated purchase payment as avalue-added service. It is to be understood that any other value-addedservice may take the place of the below-described value-added service.The user may communicate with a pay network server via a client. Forexample, the user may provide user input, e.g., 1301, into the clientindicating the user's desire to enroll in social network authenticatedpurchase payment. In various implementations, the user input mayinclude, but not be limited to: a single tap (e.g., a one-tap mobile apppurchasing embodiment) of a touchscreen interface, keyboard entry, cardswipe, activating a RFID/NFC enabled hardware device (e.g., electroniccard having multiple accounts, smartphone, tablet, etc.) within the userdevice, mouse clicks, depressing buttons on a joystick/game console,voice commands, single/multi-touch gestures on a touch-sensitiveinterface, touching user interface elements on a touch-sensitivedisplay, and/or the like. In some implementations, using the user'sinput, the client may generate an enrollment request, e.g., 1302, andprovide the enrollment request to the pay network server. In someimplementations, the EXDB may provide an enrollment button which maytake the user to an enrollment webpage where account info may be enteredinto web form fields. In some implementations, the pay network servermay obtain the enrollment request from the client, and extract theuser's payment detail from the enrollment request. For example, the paynetwork server may utilize a parser such as the example parsersdescribed below in the discussion with reference to FIG. 49. In someimplementations, the pay network server may query, e.g., 1304, a paynetwork database to obtain a social network request template, e.g.,1305, to process the enrollment request. The social network requesttemplate may include instructions, data, login URL, login API calltemplate and/or the like for facilitating social network authentication.In some implementations, the pay network server may provide paymentinformation extracted from the card authorization request to the socialnetwork server as part of a social network authentication enrollmentrequest, e.g., 1306. In some implementations, the social network servermay provide a social network login request, e.g., 1307, to the client.For example, the social network server may provide a HTML input form tothe client. The client may display, e.g., 1308, the login form for theuser. In some implementations, the user may provide login input into theclient, e.g., 1309, and the client may generate a social network loginresponse for the social network server. In some implementations, thesocial network server may authenticate the login credentials of theuser, and access payment account information of the user stored withinthe social network, e.g., in a social network database. Uponauthentication, the social network server may generate an authenticationdata record for the user, e.g., 1311, and provide an enrollmentnotification to the pay network server, e.g., 1313. Upon receivingnotification of enrollment from the social network server, the paynetwork server may generate, e.g., 1314, a user enrollment data record,and store the enrollment data record in a pay network database, e.g.,1315, to complete enrollment. The pay network server may provide anenrollment confirmation, and provide the enrollment confirmation to theclient, which may display, e.g., 1317, the confirmation for the user.

FIGS. 14A-B show flow diagrams illustrating example aspects ofnormalizing aggregated search, enrolled, service usage, transactionand/or other aggregated data into a standardized data format in someembodiments of the EXDB, e.g., a Aggregated Data Record Normalization(“ADRN”) component 1400. With reference to FIG. 14A, in someimplementations, a pay network server (“server”) may attempt to convertany aggregated data records stored in an aggregated records database ithas access to in a normalized data format. For example, the database mayhave a transaction data record template with predetermined, standardfields that may store data in pre-defined formats (e.g., longinteger/double float/4 digits of precision, etc.) in a pre-determineddata structure. A sample XML transaction data record template isprovided below:

<?XML version = “1.0” encoding = “UTF-8”?> <transaction_record><record_ID>00000000</record_ID> <norm_flag>false</norm_flag><timestamp>yyyy-mm-dd hh:mm:ss</timestamp><transaction_cost>$0,000,000,00</transaction_cost> <merchant_params><merchant_id>00000000</merchant_id> <merchant_name>TBD</merchant_name><merchant_auth_key>0000000000000000</merchant_auth_key></merchant_params> <merchant_products> <num_products>000</num_products><product> <product_type>TBD</product_type><product_name>TBD</product_name><class_labels_list>TBD<class_labels_list><product_quantity>000</product_quantity><unit_value>$0,000,000.00</unit_value><sub_total>$0,000,000.00</sub_total> <comment>normalized transactiondata record template</comment> </product> </merchant_products><user_account_params> <account_name>JTBD</account_name><account_type>TBD</account_type><account_num>0000000000000000</account_num><billing_line1>TBD</billing_line1> <billing_line2>TBD</billing_line2><zipcode>TBD</zipcode> <state>TBD</state> <country>TBD</country><phone>00-00-000-000-0000</phone> <sign>TBD</sign></user_account_params> </transaction_record>

In other embodiments, the transaction data record template may containintegrated logic, regular expressions, executable meta-commands,language commands and/or the like in order to facilitate properlymatching aggregated data with the location and format of the data in thetemplate. In some embodiments, the template may contain logic in anon-template language, such as PHP commands being included in an XMLfile. As such, in one example, a language key may be used by thetemplate (e.g., “php:<command>”, “java:<function>”, and/or the like). Inso doing, the matching template may match a vast array of disparate dataformats down into a normalized and standardized format. An exampletransaction data template record substantially in the form of XML is asfollows:

<?XML version = “1.0” encoding = “UTF-8”?> <transaction_record><record_ID default_value=false_return_error match_length=8format=integer regex_search=”(?<=\s|{circumflex over ( )})\d+(?=\s|$)”start_search_offset=”50bytes”>00000000</record_ID><norm_flag>false</norm_flag> <timestamp default_value=”MySQL:’NOW( )’”format_after_matching=”php:mktime($value);”>  yyyy-mm-ddhh:mm:ss</timestamp> <transaction_cost>$0,000,000,00</transaction_cost><merchant_params> <merch_id>00000000</merch_id><merch_name>TBD</merch_name><merch_auth_key>0000000000000000</merch_auth_key> </merchant_params><merchant_products> <num_products min_quantity=1max_quantity=30>000</num_products> <product> <product_typefrom_group=”array(‘BOOK’,’CD’,’DVD’)”> TBD</product_type><product_name>TBD</product_name><class_labels_list>TBD<class_labels_list><product_quantity>000</product_quantity><unit_value>$0,000,000.00</unit_value><sub_total>$0,000,000.00</sub_total> <comment>normalized transactiondata record template</comment> </product> </merchant_products><user_account_params> <account_name>JTBD</account_name><account_type>TBD</account_type><account_num>0000000000000000</account_num><billing_line1>TBD</billing_line1> <billing_line2>TBD</billing_line2><zipcode>TBD</zipcode> <state>TBD</state> <country>TBD</country><phone>00-00-000-000-0000</phone> <sign>TBD</sign></user_account_params> </transaction_record>

In some implementations, the server may query a database for anormalized data record template, e.g., 1401. The server may parse thenormalized data record template, e.g., 1402. In some embodiments, theparsing may parse the raw data record (such as using a parser asdescribed herein and with respect to FIG. 49). In other embodiments, theparser may parse a dictionary entry containing a subset of the completedata. Based on parsing the normalized data record template, the servermay determine the data fields included in the normalized data recordtemplate, and the format of the data stored in the fields of the datarecord template, e.g., 1403. The server may obtain transaction datarecords for normalization. The server may query a database, e.g., 1404,for non-normalized records. In one embodiment, no querying is requiredas the normalization of records may occur in flight (e.g., in real timeas data is received). For example, the server may issue PHP/SQL commandsto retrieve records that do not have the ‘norm_flag’ field from theexample template above, or those where the value of the ‘norm_flag’field is ‘false’. Upon obtaining the non-normalized transaction datarecords, the server may select one of the non-normalized transactiondata records, e.g., 1405. The server may parse the non-normalizedtransaction data record, e.g., 1406, and determine the fields present inthe non-normalized transaction data record, e.g., 1407. For example, theserver may utilize a procedure similar to one described below withreference to FIG. 15 The server may compare the fields from thenon-normalized transaction data record with the fields extracted fromthe normalized transaction data record template. For example, the servermay determine whether the field identifiers of fields in thenon-normalized transaction data record match those of the normalizedtransaction data record template, (e.g., via a dictionary, thesaurus,etc.), are identical, are synonymous, are related, and/or the like.Based on the comparison, the server may generate a 1:1 mapping betweenfields of the non-normalized transaction data record match those of thenormalized transaction data record template, e.g., 1409. The server maygenerate a copy of the normalized transaction data record template,e.g., 1410, and populate the fields of the template using values fromthe non-normalized transaction data record, e.g., 1411. The server mayalso change the value of the ‘norm_flag’ field to ‘true’ in the exampleabove. The server may store the populated record in a database (forexample, replacing the original version), e.g., 1412. The server mayrepeat the above procedure for each non-normalized transaction datarecord (see e.g., 1413), until all the non-normalized transaction datarecords have been normalized.

With reference to FIG. 14B, in some embodiments, the server may utilizemetadata (e.g., easily configurable data) to drive an analytics and ruleengine that may convert any structured data into a standardized XMLformat (“encryptmatics” XML). The encryptmatics XML may then beprocessed by an encryptmatics engine that is capable of parsing,transforming and analyzing data to generate decisions based on theresults of the analysis. Accordingly, in some embodiments, the servermay implement a metadata-based interpretation engine that parsesstructured data, including, but not limited to: web content (see e.g.,1421), graph databases (see e.g., 1422), micro bogs, images or softwarecode (see e.g., 1424), and converts the structured data into commands inthe encryptmatics XML file format. For example, the structured data mayinclude, without limitation, software code, images, free text,relational database queries, graph queries, sensory inputs (see e.g.,1423, 1425), and/or the like. A metadata based interpretation engineengine, e.g., 1426, may populate a data/command object, e.g., 1427,based on a given record using configurable metadata, e.g., 1428. Theconfigurable metadata may define an action for a given glyph or keywordcontained within a data record. The engine may then process the objectto export its data structure as a collection of encryptmatics vaults ina standard encryptmatics XML file format, e.g., 1429. The encryptmaticsXML file may then be processed to provide various features by anencryptmatics engine, e.g., 1430.

In some embodiments, the server may obtain the structured data, andperform a standardization routine using the structured data as input(e.g., including script commands, for illustration). For example, theserver may remove extra line breaks, spaces, tab spaces, etc. from thestructured data, e.g. 1431. The server may determine and load a metadatalibrary, e.g., 1432, using which the server may parse subroutines orfunctions within the script, based on the metadata, e.g., 1433-1434. Insome embodiments, the server may pre-parse conditional statements basedon the metadata, e.g., 1435-1436. The server may also parse data 1437 topopulate a data/command object based on the metadata and prior parsing,e.g., 1438. Upon finalizing the data/command object, the server mayexport 1439 the data/command object as XML in standardized encryptmaticsformat.

FIG. 15 shows a logic flow diagram illustrating example aspects ofrecognizing data fields in normalized aggregated data records in someembodiments of the EXDB, e.g., a Data Field Recognition (“DFR”)component 1500. In some implementations, a server may recognize the typeof data fields included in a data record, e.g., date, address, zipcode,name, user ID, email address, payment account number (PAN), CVV2numbers, and/or the like. The server may select an unprocessed datarecord for processing, e.g., 1501. The server may parse the data recordrule, and extract data fields from the data record, e.g., 1502. Theserver may query a database for data field templates, e.g., 1503. Forexample, the server may compare the format of the fields from the datarecord to the data record templates to identify a match between one ofthe data field templates and each field within the data record, thusidentifying the type of each field within the data record. In oneembodiment, the data field templates may be implemented as a collectionof regular expressions, a set of interpreted or compiled languagecommands that when run against the candidate match return boolean trueor false if the candidate matches, and/or the like. The server may thusselect an extracted data field from the data record, e.g., 1504. Theserver may select a data field template for comparison with the selecteddata field, e.g., 1505, and compare the data field template with theselected data field, e.g., 1506, to determine whether format ofextracted data field matches format of data field template, e.g., 1507.If the format of the selected extracted data field matches the format ofthe data field template, e.g., 1508, option “Yes,” the server may assignthe type of data field template to the selected data field, e.g., 1509.If the format of the extracted data field does not match the format ofthe data field template, e.g., 1508, option “No,” the server may tryanother data field template until no more data field templates areavailable for comparison, see e.g., 1510. If no match is found, theserver may assign “unknown” string as the type of the data field, e.g.,1511. The server may store the updated data record in the database,e.g., 1512. The server may perform such data field recognition for eachdata field in the data record (and also for each data record in thedatabase), see e.g., 1513.

FIG. 16 shows a logic flow diagram illustrating example aspects ofclassifying entity types in some embodiments of the EXDB, e.g., anEntity Type Classification (“ETC”) component 1600. In someimplementations, a server may apply one or more classification labels toeach of the data records. For example, the server may classify the datarecords according to entity type, according to criteria such as, but notlimited to: geo-political area, number of items purchased, and/or thelike. The server may obtain transactions from a database that areunclassified, e.g., 1601, and obtain rules and labels for classifyingthe records, e.g., 1602. For example, the database may storeclassification rules, such as the exemplary illustrative XML-encodedclassification rule provided below:

<rule> <id>PURCHASE_44_45</id> <name>Number of purchasers</name><inputs>num_purchasers</inputs> <operations> <1>label = ‘null’</1> <2>IF(num_purchasers > 1) label = ‘household’</2> </operations><outputs>label</outputs> </rule>

The server may select an unclassified data record for processing, e.g.,1603. The server may also select a classification rule for processingthe unclassified data record, e.g., 1604. The server may parse theclassification rule, and determine the inputs required for the rule,e.g., 1605. Based on parsing the classification rule, the server mayparse the normalized data record template, e.g., 1606, and extract thevalues for the fields required to be provided as inputs to theclassification rule. The server may parse the classification rule, andextract the operations to be performed on the inputs provided for therule processing, e.g., 1607. Upon determining the operations to beperformed, the server may perform the rule-specified operations on theinputs provided for the classification rule, e.g., 1608. In someimplementations, the rule may provide threshold values. For example, therule may specify that if the number of products in the transaction,total value of the transaction, average luxury rating of the productssold in the transaction, etc. may need to cross a threshold in order forthe label(s) associated with the rule to be applied to the transactiondata record. The server may parse the classification rule to extract anythreshold values required for the rule to apply, e.g., 1609. The servermay compare the computed values with the rule thresholds, e.g., 1610. Ifthe rule threshold(s) is crossed, e.g., 1611, option “Yes,” the servermay apply one or more labels to the transaction data record as specifiedby the classification rule, e.g., 1612. For example, the server mayapply a classification rule to an individual product within thetransaction, and/or to the transaction as a whole. In other embodiments,the rule may specify criteria that may be present in the mesh in orderto generate a new entity (e.g., to create a deduced concept or deducedentity). For example, if a given set of mesh aggregated data containreferences the a keyword iPhone, a rule may specify that “iPhone” is tobe created as a deduced node within the mesh. This may be done in arecursive manner, such as when the creation of the meta-concept of an“iPhone” may subsequently be combined with created meta-concepts of“iMac” and “iPod” in order to create a master deduced concept of “AppleComputer”, which is thereafter associated with “iPhone,” “iMac,” and“iPod”. In so doing, the rules may allow the mesh, given the aggregatedcontent available as well as inputs (such as category inputs) toautomatically create meta-concepts based on rules that are themselvesunaware of the concepts. In one embodiment, a rule for the creation of ameta-concept, substantially in the form of XML is:

<rule id=”create_deduced_concept_5” type=”deduced_concept”> <criteria><number_keyword_references> <is type=”greater_than” value=”50” /> <isnottype=”greater_than” value=”500” /> </number_keyword_references></criteria> <if_criteria_met value=”create_entity’ /> </rule>

In the example above, a new deduced entity may be added to the mesh ifthe number of other entities referencing a given keyword is greater than50 but less than 500. In one embodiment, the criteria may be specifiedas a scalar value as shown above. In other embodiments, the criteria mayreference a percentage size of the mesh references (such as greater than5% but less than 10%). In so doing, entities may be added only when theyreach a certain absolute threshold, or alternatively when they reach athreshold with respect to the mesh itself. In other embodiments, thecriteria may be a function (such as a Python procedure) that may beperformed in order to determine if a new meta-entity should be created.In such an embodiment, the rule may take advantage of any languagefeatures available (e.g., language method/functions) as well as externaldata sources (such as by querying Wikipedia for the presence of a pagedescribing the candidate meta-concept, performing a Google Search andonly creating the meta concept if greater than a given number of resultsare returned, and/or the like). In one embodiment, deduced entries maybe created based on a specified or relative frequence of occurrencematches (e.g., keyword matches, transaction occurrences, and/or thelike) within a certain time quantum (e.g., 5 orders for an item within aday/week/month, 100 tweeks a minute about a topic, and/or the like).Deduced entities may become actual mesh entities (and actual meshentities may be come deduced entities) through the application ofsimilar rules. For example, if an entity is deduced but subsequently thedata aggregation shows a sufficient social media discussion regarding adeduced concept, the concept may be changed from a deduced concept to amesh concept. In so doing, the mesh can adapt to evolving entities thatmay initially exist only by virtue of their relationship to other nodes,but may ultimately become concepts that the mesh may assign to actualentities.

In some implementations, the server may process the transaction datarecord using each rule (see, e.g., 1613). Once all classification ruleshave been processed for the transaction record, e.g., 1613, option “No,”the server may store the transaction data record in a database, e.g.,1614. The server may perform such processing for each transaction datarecord until all transaction data records have been classified (see,e.g., 1615).

FIG. 17 shows a logic flow diagram illustrating example aspects ofidentifying cross-entity correlation in some embodiments of the EXDB,e.g., a Cross-Entity Correlation (“CEC”) component 1700. In someimplementations, a server may recognize that two entities in the EXDBshare common or related data fields, e.g., date, address, zipcode, name,user ID, email address, payment account number (PAN), CVV2 numbers,and/or the like, and thus identify the entities as being correlated. Theserver may select a data record for cross-entity correlation, e.g.,1701. The server may parse the data record rule, and extract data fieldsfrom the data record, e.g., 1702-1703. The server may select anextracted data field from the data record, e.g., 1704, and query adatabase for other data records having the same data field as theextracted data field, e.g., 1705. From the list of retrieved datarecords from the database query, the server may select a record forfurther analysis. The server may identify, e.g., 1707, an entityassociated with the retrieved data record, e.g., using the ETC 1600component discussed above in the description with reference to FIG. 16.The server may add a data field to the data record obtained forcross-entity correlation specifying the correlation to the retrievedselected data record, e.g., 1708. In some embodiments, the server mayutilize each data field in the data record obtained for cross-entitycorrelation to identify correlated entities, see e.g., 1709. The servermay add, once complete, a “correlated” flag to the data record obtainedfor cross-entity correlation, e.g., 1710, e.g., along with as timestampspecifying the time at which the cross-entity correlation was performed.For example, such a timestamp may be used to determine at a later timewhether the data record should be processed again for cross-entitycorrelation. The server may store the updated data record in a database.

FIG. 18 shows a logic flow diagram illustrating example aspects ofassociating attributes to entities in some embodiments of the EXDB,e.g., an Entity Attribute Association (“EAA”) component 1800. In someimplementations, a server may associate attributes to an entity, e.g.,if the entity id a person, the server may identify a demographic (e.g.,male/female), a spend character, a purchase preferences list, amerchants preference list, and/or the like, based on field values ofdata fields in data records that are related to the entity. In someimplementations, a server may obtain a data record for entity attributeassociation, e.g., 1801. The server may parse the data record rule, andextract data fields from the data record, e.g., 1802-1803. The servermay select an extracted data field from the data record, e.g., 1804, andidentify a field value for the selected extracted data field from thedata record, e.g., 1805. The server may query a database for demographicdata, behavioral data, and/or the like, e.g., 1806, using the fieldvalue and field type. In response, the database may provide a list ofpotential attributes, as well as a confidence level in those attributeassociations to the entity, see e.g., 1807. The server may add datafields to the data record obtained for entity attribute associationspecifying the potentially associated attributes and their associatedconfidence levels, e.g., 1808. In some embodiments, the server mayutilize each data field in the data record obtained for cross-entitycorrelation to identify correlated entities, see e.g., 1809. The servermay store the updated data record in a database, e.g., 1810.

FIG. 19 shows a logic flow diagram illustrating example aspects ofupdating entity profile-graphs in some embodiments of the EXDB, e.g., anEntity Profile-Graph Updating (“EPGU”) component 1900. In someimplementations, a server may generate/update a profile for an entitywhose data is stored within the EXDB. The server may obtain an entityprofile record for updating, e.g., 1901. The server may parse the entityprofile record, and extract an entity identifier data field from thedata record, e.g., 1902. The server may query a database for other datarecords that are related to the same entity, e.g., 1903, using the valuefor the entity identifier data field. In response, the database mayprovide a list of other data records for further processing. The servermay select one of the other data records to update the entity profilerecord, e.g., 1904. The server may parse the data record, and extractall correlations, associations, and new data from the other record,e.g., 1905. The server may compare the correlations, attributes,associations, etc., from the other data record with the correlations,associations and attributes from the entity profile. Based on thiscomparison, the server may identify any new correlations, associations,etc., and generate an updated entity profile record using the newcorrelations, associations; flag new correlations, associations forfurther processing, e.g., 1907. In some embodiments, the server mayutilize each data record obtained for updating the entity profile recordas well as its social graph (e.g., as given by the correlations andassociations for the entity), see e.g., 1909. The server may store theupdated entity profile record in a database, e.g., 1908.

FIG. 20 shows a logic flow diagram illustrating example aspects ofgenerating search terms for profile-graph updating in some embodimentsof the EXDB, e.g., a Search Term Generation (“STG”) component 2000. Insome implementations, a server may generate/update a profile for anentity whose data is stored within the EXDB, by performing search fornew data, e.g., across the Internet and social networking services. Theserver may obtain an entity profile record for updating, e.g., 2001. Theserver may parse the entity profile record, and extract data field typesand field values from the entity profile record, e.g., 2002. The servermay query a database for other data records that are related to the sameentity, e.g., 2003, using the values for the extracted data fields. Inresponse, the database may provide a list of other data records forfurther processing. The server may parse the data records, and extractall correlations, associations, and data from the data records, e.g.,2004. The server may aggregate all the data values from all the recordsand the entity profile record, e.g., 2005. Based on this, the server mayreturn the aggregated data values as search terms to trigger searchprocesses (see e.g., FIG. 3, 301-305), e.g., 2006.

Electronic Virtual Wallet User Interface

FIGS. 21A-E show user interface diagrams illustrating example featuresof user interfaces for an electronic virtual wallet in some embodimentsof the EXDB. With reference to FIG. 21A, in some embodiments, a virtualwallet mobile app, e.g., 2111, executing on a device, e.g., 2100, of auser may include an app interface providing various features for theuser. For example, the device may include a camera via which the app mayacquire image frames, video data, live video, and/or the like, e.g.,2116. The app may be configured to analyze the incoming data, andsearch, e.g., 2112, for a product identifier, e.g., 2114, such asbarcodes, QR codes and/or the like.

In some embodiments, the app may be configured to automatically detect,e.g., 2112, the presence of a product identifier within an image orvideo frame grabbed by the device (e.g., via a webcam, in-built camera,etc.). For example, the app may provide a “hands-free” mode of operationwherein the user may move the device to bring product identifiers withinthe field of view of the image/video capture mechanism of the device,and the app may perform image/video processing operations toautomatically detect the product identifier within the field of view. Insome embodiments, the app may overlay cross-hairs, target box, and/orlike alignment reference markers, e.g., 2115, so that a user may alignthe product identifier using the reference markers to facilitate productidentifier recognition and interpretation.

In some embodiments, the detection of a product identifier may triggervarious operations to provide products, services, information, etc. forthe user. For example, the app may be configured to detect and capture aQR code having embedded merchant and/or product information, and utilizethe information extracted from the QR code to process a transaction forpurchasing a product from a merchant. As other examples, the app may beconfigured to provide information on related products, quotes, pricinginformation, related offers, (other) merchants related to the productidentifier, rewards/loyalty points associated with purchasing theproduct related to the product identifier, analytics on purchasingbehavior, alerts on spend tracking, and/or the like.

In some embodiments, the app may include user interface elements toallow the user to manually search, e.g., 2113, for products (e.g., byname, brand, identifier, etc.). In some embodiments, the app may providethe user with the ability to view prior product identifier captures(see, e.g., 2117 a) so that the user may be able to better decide whichproduct identifier the user desires to capture. In some embodiments, theapp may include interface elements to allow the user to switch back andforth between the product identification mode and product offerinterface display screens (see, e.g., 2117 b), so that a user mayaccurately study deals available to the user before capturing a productidentifier. In some embodiments, the user may be provided withinformation about products, user settings, merchants, offers, etc. inlist form (see, e.g., 2117 c) so that the user may better understand theuser's purchasing options. Various other features may be provided for inthe app (see, e.g., 2117 d). In some embodiments, the user may desire tocancel product purchasing; the app may provide the user with a userinterface element (e.g., 2118) to cancel the product identifierrecognition procedure and return to the prior interface screen the userwas utilizing.

With reference to FIG. 21B, in some embodiments, the app may include anindication of the location (e.g., name of the merchant store,geographical location, information about the aisle within the merchantstore, etc.) of the user, e.g., 2121. The app may provide an indicationof a pay amount due for the purchase of the product, e.g., 2122. In someembodiments, the app may provide various options for the user to pay theamount for purchasing the product(s). For example, the app may utilizeGPS coordinates associated with the device to determine the merchantstore within which the user is present, and direct the user to a websiteof the merchant. In some embodiments, the app may be configured to makean application programming interface (“API”) call to participatingmerchants to directly facilitate transaction processing for purchases.In some embodiments, a merchant-branded app may be developed with anin-app purchasing mode, which may directly connect the user into themerchant's transaction processing system. For example, the user maychoose from a number of cards (e.g., credit cards, debit cards, prepaidcards, etc.) from various card providers, e.g., 2123 a. In someembodiments, the app may provide the user the option to pay the purchaseamount using funds included in a bank account of the user, e.g., achecking, savings, money market, current account, etc., e.g., 2123 b. Insome embodiments, the user may have set default options for which card,bank account, etc. to use for the purchase transactions via the app. Insome embodiments, such setting of default options may allow the user toinitiate the purchase transaction via a single click, tap, swipe, and/orother remedial user input action, e.g., 2123 c. In some embodiments,when the user utilizes such an option, the app may utilize the defaultsettings of the user to initiate the purchase transaction. In someembodiments, the app may allow the user to utilize other accounts (e.g.,Google™ Checkout, Paypal™ account, etc.) to pay for the purchasetransaction, e.g., 2123 d. In some embodiments, the app may allow theuser to utilize rewards points, airline miles, hotel points, electroniccoupons, printed coupons (e.g., by capturing the printed coupons similarto the product identifier) etc., to pay for the purchase transaction,e.g., 2123 e. In some embodiments, the app may provide an option toprovide express authorization before initiating the purchasetransaction, e.g., 2124. In some embodiments, the app may provide aprogress indicator provide indication on the progress of the transactionafter the user has selected an option to initiate the purchasetransaction, e.g., 2125. In some embodiments, the app may provide theuser with historical information on the user's prior purchases via theapp, e.g., 2127 a. In some embodiments, the app may provide the userwith an option to share information about the purchase (e.g., via email,SMS, wall posting on Facebook®, tweet on Twitter™, etc.) with otherusers and/or control information shared with the merchant, acquirer,payment network etc., to process the purchase transaction, e.g., 2127 b.In some embodiments, the app may provide the user an option to displaythe product identification information captured by the client device(e.g., in order to show a customer service representative at the exit ofa store the product information), e.g., 2127 c. In some embodiments, theuser, app, device and or purchase processing system may encounter anerror in the processing. In such scenarios, the user may be able to chatwith a customer service representative (e.g., VerifyChat 2127 d) toresolve the difficulties in the purchase transaction procedure.

In some embodiments, the user may select to conduct the transactionusing a one-time anonymized credit card number, see e.g., 2123 f. Forexample, the app may utilize a pre-designated anonymized set of carddetails (see, e.g., “AnonCard1,” “AnonCard2”). As another example, theapp may generate, e.g., in real-time, a one-time anonymous set of carddetails to securely complete the purchase transaction (e.g., “Anon It1X”). In such embodiments, the app may automatically set the userprofile settings such that the any personal identifying information ofthe user will not be provided to the merchant and/or other entities. Insome embodiments, the user may be required to enter a user name andpassword to enable the anonymization features.

With reference to FIG. 21C, in some embodiments, the user interfaceelements of the app may be advantageously arranged to provide the userthe ability to process a purchase with customized payment parameterswith a minimum number of user inputs applied to the user's device. Forexample, if the user has a QR pay code, e.g., 2132, within the viewingangle of a camera included in the user's mobile device, the user mayactivate a user interface element to snap the QR code. In someembodiments, the user may control the field of view of the camera usinga user interface zoom element, e.g., 2133. In some embodiments, the userinterface may be designed such that the user may touch an image of a QRcode displayed on the screen to capture the QR code (see e.g., 2134).For example, the position of the user's touch may be utilized as aninput by an image processing module executing on the user's device toprocess the displayed video frame (and/or adjacent video frames), andextract the QR code from the frame(s) based on the user's input. Forexample, the user's touch may provide an approximate center point of theQR code. Using this information, the image processing module may be ableto better perform an automated QR code image recognition, andaccordingly capture the correct QR code (e.g., if portions of many QRcodes are displayed within the video frame(s)) selected by the user forcapture and processing.

In some embodiments, the app may utilize predetermined default settingsfor a particular merchant, e.g., 2131, to process the purchase based onthe QR code (e.g., in response to the user touching an image of a QRcode displayed on the screen of the user device). However, if the userwishes to customize the payment parameters, the user may activate a userinterface element 2135 (or e.g., press and continue to hold the image ofthe QR code 2132). Upon doing so, the app may provide a pop-up menu,e.g., 2137, providing a variety of payment customization choices, suchas those described with reference to FIG. 21B. The user may, e.g., dragthe user's finger to the appropriate settings the user prefers, andrelease the user's finger from the touchscreen of the user's mobiledevice to select the setting for payment processing. In alternateembodiments, the payment settings options, e.g., 2137, and QR captureactivation button, e.g., 2136 may be included in the user interfacealong with a window for capturing the QR code via the mobile device'scamera. In alternate embodiments, the user's mobile device may generatea hybrid QR code-payment settings graphic, and the POS terminal (oruser's trusted computing device) may capture the entire graphic forpayment processing. In some embodiments, the app may provide a userinterface element 2138 for the user to minimize the payment optionssettings user interface elements. In some embodiments, the app mayprovide additional user interface elements, e.g., 2139, to displayprevious purchases, data shared about those purchases, purchase receipts(e.g., via barcodes) and customer support options (e.g., VerifyChat).

With reference to FIG. 21D, in some embodiments, the user may be able toview and/or modify the user profile and/or settings of the user, e.g.,by activating user interface element 2122 (of FIG. 21B). For example,the user may be able to view/modify a user name (e.g., 2141 a-b),account number (e.g., 2142 a-b), user security access code (e.g., 2143a-b), user pin (e.g., 2144 a-b), user address (e.g., 2145 a-b), socialsecurity number associated with the user (e.g., 2146 a-b), currentdevice GPS location 27 (e.g., 2147 a-b), user account of the merchant inwhose store the user currently is (e.g., 2148 a-b), the user's rewardsaccounts (e.g., 2149 a-b), and/or the like. In some embodiments, theuser may be able to select which of the data fields and their associatedvalues should be transmitted to facilitate the purchase transaction,thus providing enhanced data security for the user. For example, in theexample illustration in FIG. 21D, the user has selected the name 2141 a,account number 2142 a, security code 2143 a, merchant account ID 2148 aand rewards account ID 2149 a as the fields to be sent as part of thenotification to process the purchase transaction. In some embodiments,the user may toggle the fields and/or data values that are sent as partof the notification to process the purchase transactions. In someembodiments, the app may provide multiple screens of data fields and/orassociated values stored for the user to select as part of the purchaseorder transmission. In some embodiments, the app may obtain the GPSlocation of the user. Based on the GPS location of the user, the app maydetermine the context of the user (e.g., whether the user is in a store,doctor's office, hospital, postal service office, etc.). Based on thecontext, the app may present the appropriate fields to the user, fromwhich the user may select fields and/or field values to send as part ofthe purchase order transmission.

For example, a user may go to doctor's office and desire to pay theco-pay for doctor's appointment. In addition to basic transactionalinformation such as account number and name, the app may provide theuser the ability to select to transfer medical records, healthinformation, which may be provided to the medical provider, insurancecompany, as well as the transaction processor to reconcile paymentsbetween the parties. In some embodiments, the records may be sent in aHealth Insurance Portability and Accountability Act (HIPAA)-compliantdata format and encrypted, and only the recipients who are authorized toview such records may have appropriate decryption keys to decrypt andview the private user information.

With reference to FIG. 21E, in some embodiments, the app executing onthe user's device may provide a “VerifyChat” feature for fraudprevention (e.g., by activating UI element 2127 d in FIG. 21B). Forexample, the EXDB may detect an unusual and/or suspicious transaction.The EXDB may utilize the VerifyChat feature to communicate with theuser, and verify the authenticity of the originator of the purchasetransaction. In various embodiments, the EXDB may send electronic mailmessage, text (SMS) messages, Facebook® messages, Twitter™ tweets, textchat, voice chat, video chat (e.g., Apple FaceTime), and/or the like tocommunicate with the user. For example, the EXDB may initiate a videochallenge for the user, e.g., 2151 a. For example, the user may need topresent him/her-self via a video chat, e.g., 2152 a. In someembodiments, a customer service representative, e.g., agent 2155 a, maymanually determine the authenticity of the user using the video of theuser. In some embodiments, the EXDB may utilize face, biometric and/orlike recognition (e.g., using pattern classification techniques) todetermine the identity of the user, e.g., 2154 a. In some embodiments,the app may provide reference marker (e.g., cross-hairs, target box,etc.), e.g., 2153 a, so that the user may the video to facilitate theEXDB's automated recognition of the user. In some embodiments, the usermay not have initiated the transaction, e.g., the transaction isfraudulent. In such embodiments, the user may cancel, e.g., 2158 a, thechallenge. The EXDB may then cancel the transaction, and/or initiatefraud investigation procedures on behalf of the user. In someembodiments, the app may provide additional user interface elements,e.g., to display previous session 2156 a, and provide additionalcustomer support options (e.g., VerifyChat 2157 a).

In some embodiments, the EXDB may utilize a text challenge procedure toverify the authenticity of the user, e.g., 2151 b. For example, the EXDBmay communicate with the user via text chat, SMS messages, electronicmail, Facebook® messages, Twitter™ tweets, and/or the like. The EXDB maypose a challenge question, e.g., 2152 b, for the user. The app mayprovide a user input interface element(s) (e.g., virtual keyboard 2153b) to answer the challenge question posed by the EXDB. In someembodiments, the challenge question may randomly selected by the EXDBautomatically; in some embodiments, a customer service representative2155 b may manually communicate with the user. In some embodiments, theuser may not have initiated the transaction, e.g., the transaction isfraudulent. In such embodiments, the user may cancel, e.g., 2158 b, thetext challenge. The EXDB may then cancel the transaction, and/orinitiate fraud investigation procedures on behalf of the user. In someembodiments, the app may provide additional user interface elements,e.g., to display previous session 2156 b, and provide additionalcustomer support options (e.g., VerifyChat 2157 b).

Merchant Analytics Platform

FIG. 22 shows a block diagram illustrating example aspects of a merchantanalytics platform in first set of embodiments of the EXDB. In someimplementations, a user, e.g., 2201, may desire to purchase productsfrom a merchant. For example, the user may utilize a card (e.g., acredit card, debit, card, prepaid card, charge card, etc.) to purchaseproducts, services, and/or other offerings (“products”) from a merchant2202. In some implementations, the user may exhibit consumptionpatterns. For example, the user may often buy a similar set of productssimultaneously each time the user shops. In some implementations, thepurchasing patterns of the user may be reflected in the cardtransactions conducted by the user. For example, the consumptionpatterns may reflect in card transaction records of the transactionsconducted by the user, which may be mined by a card company, e.g., 2203.In some implementations, information as to the general preferences ofthe user, purchasing preferences of the user, cost-sensitivities of theuser, etc. may be gleaned from studying the aggregated card transactionrecords pertaining to the user. For example, analysis of the aggregateduser card transaction records may indicate a preference for shoppingwithin a particular geographical area, at particular times, withparticular merchants, for particular products types, categories, brandnames, quantities, and/or the like. As another example, analysis of theaggregated card transaction records may indicate correlations betweenpurchases of the user. For example, the analysis may provide the abilityto predict (with a known confidence level) that a user may purchaseproduct B given that the user has purchased (or intends to purchase)product A (or products A, and/or C, and/or D, etc.). Thus, in someimplementations, analysis of the aggregated card transaction records ofa user may allow the EXDB to provide suggestions to the merchant and/oruser as to products that the user is likely to be interested inpurchasing. For example, a user may desire suggestions as to whatproducts, services, offerings, deals that user may be interested in,e.g., 2204. In some implementations, the EXDB may provide suchsuggestions, e.g., 106, to the user on a real-time basis (e.g., as theuser is scanning products at a point-of-sale terminal, as the user isperforming a price check, as the user is paying for a purchase, etc., asthe user walks by a merchant where the EXDB determines that products ofinterest to the user are available, etc.). In some implementations, amerchant, e.g., 2202, may desire to understand customer behavior betterso that the merchant may determine which products to provide forcustomers to generate maximum retail sales, generate customer loyalty,etc, e.g., 2205. In some implementations, the EXDB may provide merchantanalytics reports to the merchant including recommendations of product,service, discount, Groupon® offers, and/or other offers that themerchant can make to the user based on the user's behavioral patterns,e.g., 2206.

FIGS. 23A-B show data flow diagrams illustrating an example procedure toprovide a user and/or merchant offers for products, services and/or thelike, using user behavior patterns derived from card-based transactiondata in some embodiments of the EXDB. In some implementations, a user,e.g., 2301, may desire to purchase a product, service, offering, and/orthe like (“product”), from a merchant. The user may communicate with amerchant server, e.g., 2303, via a client such as, but not limited to: apersonal computer, mobile device, television, point-of-sale terminal,kiosk, ATM, pharmacy store, store counter, and/or the like (e.g., client2302). For example, the user may provide user input, e.g., purchaseinput 2311, into the client indicating the user's desire to purchase theproduct. In various implementations, the user input may include, but notbe limited to: keyboard entry, card swipe, activating a RFID/NFC enabledhardware device (e.g., electronic card having multiple accounts,smartphone, tablet, etc.), mouse clicks, depressing buttons on ajoystick/game console, voice commands, single/multi-touch gestures on atouch-sensitive interface, touching user interface elements on atouch-sensitive display, and/or the like. For example, the user maydirect a browser application executing on the client device to a websiteof the merchant, and may select a product from the website via clickingon a hyperlink presented to the user via the website. As anotherexample, the client may obtain track 1 data from the user's card (e.g.,credit card, debit card, prepaid card, charge card, etc.), such as theexample track 1 data provided below:

%B123456789012345{circumflex over ( )}PUBLIC/J.Q.{circumflex over( )}99011200000000000000**901******?* (wherein ‘123456789012345’ is thecard number of ‘J.Q. Public’ and has a CVV number of 901. ‘990112’ is aservice code, and *** represents decimal digits which change randomlyeach time the card is used.)

In some implementations, the client may generate a purchase ordermessage, e.g., 2312, and provide, e.g., 2313, the generated purchaseorder message to the merchant server, e.g., 2303. For example, a browserapplication executing on the client may provide, on behalf of the user,a (Secure) Hypertext Transfer Protocol (“HTTP(S)”) GET message includingthe product order details for the merchant server in the form of dataformatted according to the eXtensible Markup Language (“XML”). Below isan example HTTP(S) GET message including an XML-formatted purchase ordermessage for the merchant server:

GET /purchase.php HTTP/1.1 Host: www.merchant.com Content-Type:Application/XML Content-Length: 1306 <?XML version = “1.0” encoding =“UTF-8”?> <purchase_order> <order_ID>4NFU4RG94</order_ID><timestamp>2011-02-22 15:22:43</timestamp><user_ID>john.q.public@gmail.com</user_ID> <client_details><client_IP>192.168.23.126</client_IP><client_type>smartphone</client_type> <client_model>HTCHero</client_model> <OS>Android 2.2</OS><app_installed_flag>true</app_installed_flag> </client_details><purchase_details> <num_products>1</num_products> <product><product_type>book</product_type> <product_params> <product_title>XMLfor dummies</product_title> <ISBN>938-2-14-168710-0</ISBN> <edition>2nded.</edition> <cover>hardbound</cover> <seller>bestbuybooks</seller></product_params> <quantity>1</quantity> </product> </purchase_details><account_params> <account_name>John Q. Public</account_name><account_type>credit</account_type><account_num>123456789012345</account_num> <billing_address>123 GreenSt., Norman, OK 98765</billing_address> <phone>123-456-7809</phone><sign>/jqp/</sign> <confirm_type>email</confirm_type><contact_info>john.q.public@gmail.com</contact_info> </account_params><shipping_info> <shipping_adress>same as billing</shipping_address><ship_type>expedited</ship_type> <ship_carrier>FedEx</ship_carrier><ship_account>123-45-678</ship_account><tracking_flag>true</tracking_flag> <sign_flag>false</sign_flag></shipping_info> </purchase_order>

In some implementations, the merchant server may, in response toreceiving the purchase order message from the client, generate, e.g.,2314, a request for merchant analytics from a pay network server, e.g.,2305, so that the merchant may provide product offerings for the user.For illustration, in the example above, the merchant server may add anXML-encoded data structure to the body of the purchase order message,and forward the message to the pay network server. An exampleXML-encoded data snippet that the merchant server may add to the body ofthe purchase order message before forwarding to the pay network serveris provided below:

<analytics_request> <request_ID>NEUI4BGF9</request_ID> <details><type>products OR services OR discounts</type> <deliver_to>user ANDmerchant</deliver_to> <timeframe>realtime</timeframe><user_priority>high</user_priority> <data_source>appended</data_source></details> <merchant_params> <merchant_ID>3FBCR4INC</merchant_id><merchant_name>Books & Things, Inc.</merchant_name><merchant_auth_key>1NNF484MCP59CHB27365</merchant_auth_ke y></merchant_params> </analytics_request>

The merchant server may provide the merchant analytics request, e.g.,2315, to the pay network server. In some implementations, the paynetwork server may extract the merchant and user profile informationfrom the merchant analytics request. For illustration, the pay networkserver may extract values of the ‘merchant_ID’ and ‘user_ID’ fields fromthe merchant analytics request in the examples above. Using the merchantand user profile information, the pay network server may determinewhether the merchant and/or user are enrolled in the merchant analyticsprogram. In some implementations, the pay network server may provide theresults of merchant analytics only to those entities that are enrolledin the merchant analytics program. For example, the server may query adatabase, e.g., pay network database 2307, to determine whether the userand/or merchant are enrolled in the merchant analytics program. In someimplementations, the pay network server may generate a query thedatabase for user behavior patterns of the user for merchant analytics,e.g., 2317. For example, the database may be a relational databaseresponsive to Structured Query Language (“SQL”) commands. The paynetwork server may execute a hypertext preprocessor (“PHP”) scriptincluding SQL commands to query the database for user behavior patternsof the user. An example PHP/SQL command listing, illustratingsubstantive aspects of querying the database, is provided below:

<?PHP header(′Content-Type: text/plain′);mysql_connect(“254.93.179.112”,$DBserver,$password); // access databaseserver mysql_select_db(“USERS.SQL”); // select database table to search//create query for issuser server data $query = “SELECTbehavior_profile_XML FROM UserBehaviorTable WHERE userid LIKE ′%′$user_id”; $result = mysql_query($query); // perform the search querymysql_close(“USERS.SQL”); // close database access ?>

In response to obtaining the issuer server query, e.g., 2317, the paynetwork database may provide, e.g., 2318, the requested behaviorpatterns data to the pay network server. For example, the user behaviorpatterns data may comprise pair-wise correlations of various variablesto each other, and/or raw user transaction patterns. An exampleXML-encoded user behavior pattern data file is provided below:

<?XML version = “1.0” encoding = “UTF-8”?> <last_updated>2011-02-2215:22:43</timestamp> <user_ID>john.q.public@gmail.com</user_ID><pair_correlation_data><pair><time>AM</time><pdt>A</pdt><confidence>0.65</confid ence></pair><pair><pdt>B</pdt><pdt>A</pdt><confidence>0.95</confidenc e></pair><pair><zip>98456</zip><pdt>A</pdt><confidence>0.25</confi dence></pair><pair><time>PM</time><zip>98465</zip><confidence>0.45</confidence></pair> </pair_correlation_data> <raw_data> <transaction><timestamp>2011-02-21 15:32:01</timestamp> <product><product_type>book</product_type> <product_params> <product_title>XMLfor dummies</product_title> <ISBN>938-2-14-168710-0</ISBN> <edition>2nded.</edition> <cover>hardbound</cover> <seller>bestbuybooks</seller></product_params> <quantity>1</quantity> </transaction> . . .<transaction> . . . </transaction> </raw_data>

In some implementations, the pay network server may identify products,services and/or other offerings likely desired by the user based onpre-generated user behavioral pattern analysis and user profile, e.g.,2319. The pay network server may generate a query, e.g., 2320, formerchants that may be able to provide the identified products, services,and/or offerings for the user. For example, the pay network server maygenerate a query based on the GPS coordinates of the user (e.g.,obtained from the user's smartphone), the merchant store in which theuser currently is present, etc., for merchants in the vicinity of theuser who may have products included within the identified productslikely desired by the user. In some implementations, the pay networkserver may also generate a query for offers (e.g., discount offers,Groupon® offers, etc.) that the merchant may be able to offer for theusers. For example, the pay network server may utilize PHP/SQL commandssimilar to those provided above to query a database. In response, thedatabase may provide, e.g., 2321, the requested merchant and/or offerdata to the pay network server. In some implementations, the pay networkserver may generate a real-time merchant analytics report for themerchant, e.g., 2322. In some implementations, the pay network servermay generate a real-time geo-sensitive product offer packet for theuser, e.g., including such items as (but not limited to): merchantnames, location, directions, offers, discounts, interactive onlinepurchase options, instant mobile wallet purchase ability, order holdplacing features (e.g., to hold the items for pick up so as to preventthe items going out of stock, e.g., during seasonal shopping times),and/or the like. In some implementations, the pay network server mayprovide the merchant analytics report, e.g., 2324, to the merchantserver, and may provide the real-time geo-sensitive product offerpacket, e.g., 2327, to the client. In some implementations, the merchantserver may utilize the pay network server's merchant analytics report togenerate, e.g., 2325, offer(s) for the user. The merchant server mayprovide the generated offer(s), e.g., 2326, to the user. In someimplementations, the client may render and display, e.g., 2328, thereal-time geo-sensitive product offer packet from the pay network serverand/or purchase offer(s) from the merchant to the user.

FIG. 24 shows a logic flow diagram illustrating example aspects ofproviding a user and/or merchant offers for products, services and/orthe like, using user behavior patterns derived from card-basedtransaction data in some embodiments of the EXDB, e.g., a MerchantAnalytics (“MA”) component. In some implementations, the EXDB may obtaina trigger to perform merchant analytics. For example a user may desireto purchase a product, service, offering, and/or the like (“product”),from a merchant (e.g., start scanning products in the checkout counterof the merchant's store), or may initiate a purchase transaction (e.g.,attempt to pay for products purchased at the merchant store). In someimplementations, the EXDB may extract, e.g., 2402, the merchant and userprofile information from the merchant analytics request. For example,the EXDB may extract fields such as, but not limited to: user_ID,user_name, timestamp, merchant_ID, merchant_name, merchant_type, and/orthe like. Using the merchant and/or user profile information, the EXDBmay generate a query the database for user behavior patterns, e.g.,2403, of the user for merchant analytics. In some implementations, theEXDB may identify products, services and/or other offerings likelydesired by the user based on pre-generated user behavioral patternanalysis and user profile, e.g., 2404. The EXDB may identify, e.g.,2405, merchants that may be able to provide the identified products,services, and/or offerings for the user. For example, the EXDB maygenerate a query based on the GPS coordinates of the user (e.g.,obtained from the user's smartphone), the merchant store in which theuser currently is present, etc., for merchants in the vicinity of theuser who may have products included within the identified productslikely desired by the user. In some implementations, the pay networkserver may also determine offers (e.g., discount offers, Groupon®offers, etc.), e.g., 2406, that the merchant may be able to offer forthe users. In some implementations, the EXDB may generate a real-timemerchant analytics report for the merchant, e.g., 2407. In someimplementations, the EXDB may generate, e.g., 2408, a real-timegeo-sensitive product offer packet for the user, e.g., including suchitems as (but not limited to): merchant names, location, directions,offers, discounts, interactive online purchase options, instant mobilewallet purchase ability, order hold placing features (e.g., to hold theitems for pick up so as to prevent the items going out of stock, e.g.,during seasonal shopping times), and/or the like. In someimplementations, the EXDB may provide the merchant analytics report tothe merchant server, and may provide the real-time geo-sensitive productoffer packet to the client, e.g., 2409.

FIG. 25 shows a logic flow diagram illustrating example aspects ofgenerating a user behavior pattern analysis in some embodiments of theEXDB, e.g., a User Behavioral Pattern Analytics (“UBPA”) component. Insome implementations, the EXDB may select, e.g., 2501, a user (e.g., viauser ID) for behavioral pattern analysis. The EXDB may store, e.g.,2502, card-based transaction data records for each card-basedtransaction performed by the user, e.g., via a Card-Based TransactionExecution component. The EXDB may aggregate such card-based transactiondata records of the user, e.g., 2503. For example, the EXDB may utilizea Transaction Data Aggregation component such as that described abovewith reference to FIGS. 8-9. In various implementations, the EXDB mayaggregate card transaction records of the user according to criteriaincluding, but not limited to: geographical location of card use, timeof card use, type of purchase, quantity of purchase, transaction value,merchant type, merchant name, spending category (e.g., such as the NorthAmerican Industry Classification System (NAICS) codes for spendingcategories), and/or the like. The EXDB may analyze the aggregated cardtransaction data, e.g., 2504, to determine user behavioral patterns,e.g., via a User Pattern Identification (“UPI”) component such asdescribed below with reference to FIG. 26. In some implementations, theEXDB may provide user behavioral patterns obtained from the analysis foruse by other EXDB components and/or affiliated entities, e.g., 2505.

FIG. 26 shows a logic flow diagram illustrating example aspects ofidentifying user behavioral patterns from aggregated card-basedtransaction data in some embodiments of the EXDB, e.g., a User PatternIdentification (“UPI”) component. In some implementations, a pay networkserver (“server”) may obtain a user ID of a user for whom the server isrequired to generate user behavioral patterns, e.g., 2601. The servermay query a database, e.g., a pay network database, for aggregated cardtransaction data records of the user, e.g., 2602. The server may alsoquery, e.g., 2603, the pay network database for all possible field valuethat can be taken by each of the field values (e.g., AM/PM, zipcode,merchant_ID, merchant_name, transaction cost brackets, etc.). Using thefield values of all the fields in the transaction data records, theserver may generate field value pairs, for performing a correlationanalysis on the field value pairs, e.g., 2604. An example field valuepair is: ‘time’ is ‘AM’ and ‘merchant’ is ‘Walmart’. The server may thengenerate probability estimates for each field value pair occurring inthe aggregated transaction data records. For example, the server mayselect a field value pair, e.g., 2605. The server may determine thenumber of records within the aggregated transaction data records wherethe field value pair occurs, e.g., 2606. The server may then calculate aprobability quotient for the field value pair by dividing the numberdetermined for the occurrences of the field value pair by the totalnumber of aggregate transaction data records, e.g., 2607. The server mayalso assign a confidence level for the probability quotient based on thesample size, e.g., total number of records in the aggregated transactiondata records, e.g., 2608. The server may generate and store an XMLsnippet, such as described above with reference to FIGS. 23A-B,including the field value pair, the probability quotient, and theconfidence level associated with the probability quotient, e.g., 2609.The server may perform such a computation for each field value pair (see2610) generated in 2604.

FIGS. 27A-B show block diagrams illustrating example aspects of merchantanalytics in a second set of embodiments of the EXDB. With reference toFIG. 27A, in some implementations, the EXDB may provide merchantanalytics reports to various users in order to facilitate their makingcalculated investment decisions. For example, a stock investor maydesire business analytics to determine which stocks the investor shouldinvest in, how the investor should modify the investor's portfolio,and/or the like, e.g., 2701. In another example, a retailer may desireto understand customer behavior better so that the retailer maydetermine which products to provide for customers to generate maximumretail sales, e.g., 2702. In another example, a serviceperson providingservices to customers may desire to understand which services thecustomer tend to prefer, and/or a paying for in the marketplace, e.g.,2703. In another example, a service provider may desire to understandthe geographical areas where business for the serviceperson is likely tobe concentrated, e.g., 2704. In some implementations, a credit cardcompany may have access to a large database of card-based transactions.The card-based transaction may have distributed among them informationon customer behavior, demand, geographical distribution, industry sectorpreferences, and/or the like, which may be mined in order to provideinvestors, retailer, service personnel and/or other users businessanalytics information based on analyzing the card-based transactiondata. In some implementations, the EXDB may take specific measures inorder to ensure the anonymity of users whose card-based transaction dataare analyzed for providing business analytics information for users. Forexample, the EXDB may perform business analytics on anonymizedcard-based transaction data to provide solutions to questions such asillustrated in 2701-2704.

With reference to FIG. 27B, in some implementations, the EXDB may obtainan investment strategy to be analyzed, e.g., 2711, for example, from auser. The EXDB may determine, e.g., 2712 the scope of the investmentstrategy analysis (e.g., geographic scope, amount of data required,industry segments to analyze, type of analysis to be generated,time-resolution of the analysis (e.g., minute, hour, day, month, year,etc.), geographic resolution (e.g., street, block, zipcode, metropolitanarea, city, state, country, inter-continental, etc.). The EXDB mayaggregate card-based transaction data in accordance with the determinedscope of analysis, e.g., 2713. The EXDB may normalized aggregatedcard-based transaction data records for uniform processing, e.g., 2714.In some implementations, the EXDB may apply classification labels tocard-based transaction data records, e.g., 2715, for investment strategyanalysis. The EXDB may filter the card-based transaction data records toinclude only those records as relevant to the analysis, e.g., 2716. Forexample, the EXDB may utilize the classification labels corresponding tothe transaction data records to determine which records are relevant tothe analysis. In some implementations, the EXDB may anonymizetransaction data records for consumer privacy protection prior toinvestment strategy analysis, e.g., 2717. The EXDB may performeconometrical investment strategy analysis, e.g., 2718, and generate aninvestment strategy analysis report based on the investment strategyanalysis, e.g., 2719. The EXDB may provide the investment strategyanalysis report for the user requesting the investment strategyanalysis.

FIGS. 28A-C show data flow diagrams illustrating an example procedurefor econometrical analysis of a proposed investment strategy based oncard-based transaction data in some embodiments of the EXDB. Withreference to FIG. 28A, in some implementations, a user, e.g., 2801, maydesire to obtain an analysis of an investment strategy. For example, theuser may be a merchant, a retailer, an investor, a serviceperson, and/orthe like provider or products, services, and/or other offerings. Theuser may communicate with a pay network server, e.g., 2805 a, to obtainan investment strategy analysis. For example, the user may provide userinput, e.g., analysis request input 2811, into a client, e.g., 2802,indicating the user's desire to request an investment strategy analysis.In various implementations, the user input may include, but not belimited to: keyboard entry, mouse clicks, depressing buttons on ajoystick/game console, voice commands, single/multi-touch gestures on atouch-sensitive interface, touching user interface elements on atouch-sensitive display, and/or the like. In some implementations, theclient may generate an investment strategy analysis request, e.g., 2812,and provide, e.g., 2813, the generated investment strategy analysisrequest to the pay network server. For example, a browser applicationexecuting on the client may provide, on behalf of the user, a (Secure)Hypertext Transfer Protocol (“HTTP(S)”) GET message including theinvestment strategy analysis request in the form of XML-formatted data.Below is an example HTTP(S) GET message including an XML-formattedinvestment strategy analysis request:

GET /analysisrequest.php HTTP/1.1 Host: www.paynetwork.com Content-Type:Application/XML Content-Length: 1306 <?XML version = “1.0” encoding =“UTF-8”?> <analysis_request> <request_ID>EJ39FI1F</request_ID><timestamp>2011-02-24 09:08:11</timestamp><user_ID>investor@paynetwork.com</user_ID> <password>******</password><request_details> <time_period>year 2011</time_period><time_interval>month-to-month</time_interval> <area_scope>UnitedStates</area> <area_resolution>zipcode</area_resolution><spend_sector>retail<sub>home improvement</sub></spend_sector></request_details> <client_details><client_IP>192.168.23.126</client_IP><client_type>smartphone</client_type> <client_model>HTCHero</client_model> <OS>Android 2.2</OS><app_installed_flag>true</app_installed_flag> </client_details></analysis_request>

In some implementations, the pay network server may parse the investmentstrategy analysis request, and determine the type of investment strategyanalysis required, e.g., 2814. In some implementations, the pay networkserver may determine a scope of data aggregation required to perform theanalysis. The pay network server may initiate data aggregation based onthe determined scope, for example, via a Transaction Data Aggregation(“TDA”) component such as described above with reference to FIG. 9. Thepay network server may query, e.g., 2816, a pay network database, e.g.,2807, for addresses of pay network servers that may have storedtransaction data within the determined scope of the data aggregation.For example, the pay network server may utilize PHP/SQL commands similarto the examples provided above. The database may provide, e.g., 2817, alist of server addresses in response to the pay network server's query.Based on the list of server addresses, the pay network server may issuetransaction data requests, e.g., 2818 b-n, to the other pay networkservers, e.g., 2805 b-n. The other the pay network servers may querytheir transaction databases, e.g., 2810 b-n, for transaction datafalling within the scope of the transaction data requests. In responseto the transaction data queries, e.g., 2819 b-n, the transactiondatabases may provide transaction data, e.g., 2820 b-n, to the other paynetwork servers. The other pay network servers may return thetransaction data obtained from the transactions databases, e.g., 2821b-n, to the pay network server making the transaction data requests,e.g., 2805 a.

With reference to FIG. 28B, the pay network server 2805 a may aggregate,e.g., 2823, the obtained transaction data records, e.g. via the TDAcomponent. The pay network server may normalize, e.g., 2824, theaggregated transaction data so that all the data share a uniform datastructure format, e.g., via a Transaction Data Normalization (“TDN”)component such as described below with reference to FIG. 29. The paynetwork server may generate, e.g., 2825-2828, one or more classificationlabels for each of the transaction data records, e.g., via a Card-BasedTransaction Classification (“CTC”) component such as described belowwith reference to FIG. 30. The pay network server may query forclassification rules, e.g., 2826, a database, e.g., pay network database2807. Upon obtaining the classification rules, e.g., 2827, the paynetwork server may generate, e.g., 2828, classified transaction datarecords using the classification rules, e.g., via the CTC component. Thepay network server may filter, e.g., 2829, relevant transaction datarecords using the classification labels, e.g., via a Transaction DataFiltering (“TDF”) component such as described below with reference toFIG. 31. The pay network server may anonymize, e.g., 2830, thetransaction data records, e.g., via a Consumer Data Anonymization(“CDA”) component such as described below with reference to FIG. 32.

With reference to FIG. 28C, the pay network server may, in someimplementations, store aggregated, normalized, classified, filtered,and/or anonymized data records, e.g., 2832, in a database, e.g.,transactions database 2810 a. In some implementations, the pay networkserver may econometrically analyze, e.g., 2833, aggregated, normalized,classified, filtered, and/or anonymized data records, e.g., via anEconometrical Strategy Analysis (“ESA”) component such as describedbelow with reference to FIG. 33. The pay network server may prepare areport customized to the client used by the user. The pay network servermay provide a reporting rules query to a database, e.g., pay networkdatabase 2807, for reporting rules to use in preparing the businessanalytics report. Upon obtaining the reporting rules, e.g., 2835, thepay network server may generate a business analytics report customizedto the client, e.g., 2836, for example via a Business AnalyticsReporting (“BAR”) such as described below with reference to FIG. 34. Thepay network server may provide the business analytics report, e.g.,2837, to the client, e.g., 2802. The client may render and display,e.g., 2838, the business analytics report for the user.

FIG. 29 shows a logic flow diagram illustrating example aspects ofnormalizing raw card-based transaction data into a standardized dataformat in some embodiments of the EXDB, e.g., a Transaction DataNormalization (“TDN”) component. In some implementations, a pay networkserver (“server”) may attempt to convert any transaction data recordsstored in a database it has access to in a normalized data format. Forexample, the database may have a transaction data record template withpredetermined, standard fields that may store data in pre-definedformats (e.g., long integer/double float/4 digits of precision, etc.) ina pre-determined data structure. A sample XML transaction data recordtemplate is provided below:

<?XML version = “1.0” encoding = “UTF-8”?> <transaction_record><record_ID>00000000</record_ID> <norm_flag>false</norm_flag><timestamp>yyyy-mm-dd hh:mm:ss</timestamp><transaction_cost>$0,000,000,00</transaction_cost> <merchant_params><merchant_id>00000000</merchant_id> <merchant_name>TBD</merchant_name><merch_auth_key>0000000000000000</merch_auth_key> </merchant_params><merchant_products> <num_products>000</num_products> <product><product_type>TBD</product_type> <product_name>TBD</product_name><class_labels_list>TBD<class_labels_list><product_quantity>000</product_quantity><unit_value>$0,000,000.00</unit_value><sub_total>$0,000,000.00</sub_total> <comment>normalized transactiondata record template</comment> </product> </merchant_products><user_account_params> <account_name>JTBD</account_name><account_type>TBD</account_type><account_num>0000000000000000</account_num><billing_line1>TBD</billing_line1> <billing_line2>TBD</billing_line2><zipcode>TBD</zipcode> <state>TBD</state> <country>TBD</country><phone>00-00-000-000-0000</phone> <sign>TBD</sign></user_account_params> </transaction_record>

In some implementations, the server may query a database for anormalized transaction data record template, e.g., 2901. The server mayparse the normalized data record template, e.g., 2902. Based on parsingthe normalized data record template, the server may determine the datafields included in the normalized data record template, and the formatof the data stored in the fields of the data record template, e.g.,2903. The server may obtain transaction data records for normalization.The server may query a database, e.g., 2904, for non-normalized records.For example, the server may issue PHP/SQL commands to retrieve recordsthat do not have the ‘norm_flag’ field from the example template above,or those where the value of the ‘norm_flag’ field is ‘false’. Uponobtaining the non-normalized transaction data records, the server mayselect one of the non-normalized transaction data records, e.g., 2905.The server may parse the non-normalized transaction data record, e.g.,2906, and determine the fields present in the non-normalized transactiondata record, e.g., 2907. The server may compare the fields from thenon-normalized transaction data record with the fields extracted fromthe normalized transaction data record template. For example, the servermay determine whether the field identifiers of fields in thenon-normalized transaction data record match those of the normalizedtransaction data record template, (e.g., via a dictionary, thesaurus,etc.), are identical, are synonymous, are related, and/or the like.Based on the comparison, the server may generate a 1:1 mapping betweenfields of the non-normalized transaction data record match those of thenormalized transaction data record template, e.g., 2909. The server maygenerate a copy of the normalized transaction data record template,e.g., 2910, and populate the fields of the template using values fromthe non-normalized transaction data record, e.g., 2911. The server mayalso change the value of the ‘norm_flag’ field to ‘true’ in the exampleabove. The server may store the populated record in a database (forexample, replacing the original version), e.g., 2912. The server mayrepeat the above procedure for each non-normalized transaction datarecord (see e.g., 2913), until all the non-normalized transaction datarecords have been normalized.

FIG. 30 shows a logic flow diagram illustrating example aspects ofgenerating classification labels for card-based transactions in someembodiments of the EXDB, e.g., a Card-Based Transaction Classification(“CTC”) component. In some implementations, a server may apply one ormore classification labels to each of the transaction data records. Forexample, the server may classify the transaction data records, accordingto criteria such as, but not limited to: geo-political area, luxurylevel of the product, industry sector, number of items purchased in thetransaction, and/or the like. The server may obtain transactions from adatabase that are unclassified, e.g., 3001, and obtain rules and labelsfor classifying the records, e.g., 3002. For example, the database maystore classification rules, such as the exemplary illustrativeXML-encoded classification rule provided below:

<rule> <id>NAICS44_45</id> <name>NAICS - Retail Trade</name><inputs>merchant_id</inputs> <operations> <1>label = ‘null’</1> <1>cat =NAICS_LOOKUP(merchant_id)</1> <2>IF (cat == 44 || cat ==45) label =‘retail trade’</2> </operations> <outputs>label</outputs> </rule>

The server may select an unclassified data record for processing, e.g.,3003. The server may also select a classification rule for processingthe unclassified data record, e.g., 3004. The server may parse theclassification rule, and determine the inputs required for the rule,e.g., 3005. Based on parsing the classification rule, the server mayparse the normalized data record template, e.g., 3006, and extract thevalues for the fields required to be provided as inputs to theclassification rule. For example, to process the rule in the exampleabove, the server may extract the value of the field ‘merchant_id’ fromthe transaction data record. The server may parse the classificationrule, and extract the operations to be performed on the inputs providedfor the rule processing, e.g., 3007. Upon determining the operations tobe performed, the server may perform the rule-specified operations onthe inputs provided for the classification rule, e.g., 3008. In someimplementations, the rule may provide threshold values. For example, therule may specify that if the number of products in the transaction,total value of the transaction, average luxury rating of the productssold in the transaction, etc. may need to cross a threshold in order forthe label(s) associated with the rule to be applied to the transactiondata record. The server may parse the classification rule to extract anythreshold values required for the rule to apply, e.g., 3009. The servermay compare the computed values with the rule thresholds, e.g., 3010. Ifthe rule threshold(s) is crossed, e.g., 3011, option “Yes,” the servermay apply one or more labels to the transaction data record as specifiedby the classification rule, e.g., 3012. For example, the server mayapply a classification rule to an individual product within thetransaction, and/or to the transaction as a whole. In someimplementations, the server may process the transaction data recordusing each rule (see, e.g., 3013). Once all classification rules havebeen processed for the transaction record, e.g., 3013, option “No,” theserver may store the transaction data record in a database, e.g., 3014.The server may perform such processing for each transaction data recorduntil all transaction data records have been classified (see, e.g.,3015).

FIG. 31 shows a logic flow diagram illustrating example aspects offiltering card-based transaction data for econometrical investmentstrategy analysis in some embodiments of the EXDB, e.g., a TransactionData Filtering (“TDF”) component. In some implementations, a server mayfilter transaction data records prior to econometrical investmentstrategy analysis based on classification labels applied to thetransaction data records. For example, the server may filter thetransaction data records, according to criteria such as, but not limitedto: geo-political area, luxury level of the product, industry sector,number of items purchased in the transaction, and/or the like. Theserver may obtain transactions from a database that are classified,e.g., 3101, and investment strategy analysis parameters, e.g., 3102.Based on the analysis parameters, the server may generate filter rulesfor the transaction data records, e.g., 3103. The server may select aclassified data record for processing, e.g., 3104. The server may alsoselect a filter rule for processing the classified data record, e.g.,3105. The server may parse the filter rule, and determine theclassification labels required for the rule, e.g., 3106. Based onparsing the classification rule, the server may parse the classifieddata record, e.g., 3107, and extract values for the classificationlabels (e.g., true/false) required to process the filter rule. Theserver may apply the classification labels values to the filter rule,e.g., 3108, and determine whether the transaction data record passes thefilter rule, e.g., 3109. If the data record is admissible in view of thefilter rule, e.g., 3110, option “Yes,” the server may store thetransaction data record for further analysis, e.g., 3112. If the datarecord is not admissible in view of the filter rule, e.g., 3110, option“No,” the server may select another filter rule to process thetransaction data record. In some implementations, the server may processthe transaction data record using each rule (see, e.g., 3111) until allrules are exhausted. The server may perform such processing for eachtransaction data record until all transaction data records have beenfiltered (see, e.g., 3113).

FIG. 32 shows a logic flow diagram illustrating example aspects ofanonymizing consumer data from card-based transactions for econometricalinvestment strategy analysis in some embodiments of the EXDB, e.g., aConsumer Data Anonymization (“CDA”) component. In some implementations,a server may remove personal information relating to the user (e.g.,those fields that are not required for econometrical investment strategyanalysis) and/or merchant from the transaction data records. Forexample, the server may truncate the transaction data records, fillrandomly generated values in the fields comprising personal information,and/or the like. The server may obtain transactions from a database thatare to be anonymized, e.g., 3201, and investment strategy analysisparameters, e.g., 3202. Based on the analysis parameters, the server maydetermine the fields that are necessary for econometrical investmentstrategy analysis, e.g., 3203. The server may select a transaction datarecord for processing, e.g., 3204. The server may parse the transactiondata record, e.g., 3205, and extract the data fields in the transactionsdata records. The server may compare the data fields of the transactiondata record with the fields determined to be necessary for theinvestment strategy analysis, e.g., 3206. Based on the comparison, theserver may remove any data fields from the transaction data record,e.g., those that are not necessary for the investment strategy analysis,and generate an anonymized transaction data record, e.g., 3207. Theserver may store the anonymized transaction data record in a database,e.g., 3208. In some implementations, the server may process eachtransaction data record (see, e.g., 3209) until all the transaction datarecords have been anonymized.

FIGS. 33A-B show logic flow diagrams illustrating example aspects ofeconometrically analyzing a proposed investment strategy based oncard-based transaction data in some embodiments of the EXDB, e.g., anEconometrical Strategy Analysis (“ESA”) component. With reference toFIG. 33A, in some implementations, the server may obtain spendingcategories (e.g., spending categories as specified by the North AmericanIndustry Classification System (“NAICS”)) for which to generateestimates, e.g., 3301. The server may also obtain the type of forecast(e.g., month-to-month, same-month-prior-year, yearly, etc.) to begenerated from the econometrical investment strategy analysis, e.g.,3302. In some implementations, the server may obtain the transactiondata records using which the server may perform econometrical investmentstrategy analysis, e.g., 3303. For example, the server may select aspending category (e.g., from the obtained list of spending categories)for which to generate the forecast, e.g., 3304. For example, theforecast series may be several aggregate series (described below) andthe 12 spending categories in the North American Industry ClassificationSystem (NAICS) such as department stores, gasoline, and so on, that maybe reported by the Department of Commerce (DOC).

To generate the forecast, the server may utilize a random sample oftransaction data (e.g., approximately 6% of all transaction data withinthe network of pay servers), and regression analysis to generate modelequations for calculating the forecast from the sample data. Forexample, the server may utilize distributed computing algorithms such asGoogle MapReduce. Four elements may be considered in the estimation andforecast methodologies: (a) rolling regressions; (b) selection of thedata sample (“window”) for the regressions; (c) definition ofexplanatory variables 12 (selection of accounts used to calculatespending growth rates); and (d) inclusion of the explanatory variablesin the regression equation (“candidate” regressions) that may beinvestigated for forecasting accuracy. The dependent variable may be,e.g., the growth rate calculated from DOC revised sales estimatespublished periodically. Rolling regressions may be used as a stable andreliable forecasting methodology. A rolling regression is a regressionequation estimated with a fixed length data sample that is updated withnew (e.g., monthly) data as they become available. When a new dataobservation is added to the sample, the oldest observation is dropped,causing the total number of observations to remain unchanged. Theequation may be estimated with the most recent data, and may bere-estimated periodically (e.g., monthly). The equation may then be usedto generate a one-month ahead forecast for year-over-year or month overmonth sales growth.

Thus, in some implementations, the server may generate N window lengths(e.g., 18 mo, 24 mo, 36 mo) for rolling regression analysis, e.g., 3305.For each of the candidate regressions (described below), various windowlengths may be tested to determine which would systemically provide themost accurate forecasts. For example, the server may select a windowlength may be tested for rolling regression analysis, e.g., 3306. Theserver may generate candidate regression equations using seriesgenerated from data included in the selected window, e.g., 3307. Forexample, the server may generate various series, such as, but notlimited to:

Series (1): Number of accounts that have a transaction in the selectedspending category in the current period (e.g., month) and in the priorperiod (e.g., previous month/same month last year);

Series (2): Number of accounts that have a transaction in the selectedspending category in the either the current period (e.g., month), and/orin the prior period (e.g., previous month/same month last year);

Series (3): Number of accounts that have a transaction in the selectedspending category in the either the current period (e.g., month), or inthe prior period (e.g., previous month/same month last year), but notboth;

Series (4): Series (1)+overall retail sales in any spending categoryfrom accounts that have transactions in both the current and priorperiod;

Series (5): Series (1)+Series (2)+overall retail sales in any spendingcategory from accounts that have transactions in both the current andprior period; and

Series (6): Series (1)+Series (2)+Series (3)+overall retail sales in anyspending category from accounts that have transactions in both thecurrent and prior period.

With reference to FIG. 33B, in some implementations, the server maycalculate several (e.g., six) candidate regression equations for each ofthe series. For example, the server may calculate the coefficients foreach of the candidate regression equations. The server may calculate avalue of goodness of fit to the data for each candidate regressionequations, e.g., 3308. For example, two measures of goodness of fit maybe used: (1) out-of-sample (simple) correlation; and (2) minimumabsolute deviation of the forecast from revised DOC estimates. In someimplementations, various measures of goodness of fit may be combined tocreate a score. In some implementations, candidate regression equationsmay be generated using rolling regression analysis with each of the Ngenerated window lengths (see, e.g., 3309). In some implementations,upon generation of all the candidate regression equations and theircorresponding goodness of fit scores, the equation (s) with the bestscore is chosen as the model equation for forecasting, e.g., 3310. Insome implementations, the equation(s) with the highest score is thenre-estimated using latest retail data available, e.g., from the DOC,e.g., 3311. The rerun equations may be tested for auto correlatederrors. If the auto correlation test is statistically significant thenthe forecasts may include an autoregressive error component, which maybe offset based on the autocorrelation test.

In some implementations, the server may generate a forecast for aspecified forecast period using the selected window length and thecandidate regression equation, e.g., 3312. The server may create finalestimates for the forecast using DOC estimates for prior period(s),e.g., 3313. For example, the final estimates (e.g., F_(t)^(Y)—year-over-year growth, F_(t) ^(M)—month-over-month growth) may becalculated by averaging month-over-month and year-over-year estimates,as follows:

D _(t) ^(Y)=(1+G _(t) ^(Y))·R _(t-12)

D _(t) ^(M)=(1+G _(t) ^(M))·A _(t-1)

D _(t)=Mean(D _(t) ^(M) ,D _(t) ^(Y))

B _(t-1) ^(Y)=(1+G _(t-1) ^(Y))·R _(t-13)

B _(t-1) ^(M) =A _(t-1)

B _(t-1)=Mean(B _(t-1) ^(M) ,B _(t-1) ^(Y))

F _(t) ^(Y) =D _(t) /R _(t-12)−1

F _(t) ^(M) =D _(t) /B _(t-1)−1

Here, G represents the growth rates estimated by the regressions foryear (superscript Y) or month (superscript M), subscripts refer to theestimate period, t is the current forecasting period); R represents theDOC revised dollar sales estimate; A represents the DOC advance dollarestimate; D is a server-generated dollar estimate, B is a base dollarestimate for the previous period used to calculate the monthly growthforecast.

In some implementations, the server may perform a seasonal adjustment tothe final estimates to account for seasonal variations, e.g., 3314. Forexample, the server may utilize the X-12 ARIMA statistical program usedby the DOC for seasonal adjustment. The server may then provide thefinalized forecast for the selected spending category, e.g., 3315.Candidate regressions may be similarly run for each spending category ofinterest (see, e.g., 3316).

FIG. 34 shows a logic flow diagram illustrating example aspects ofreporting business analytics derived from an econometrical analysisbased on card-based transaction data in some embodiments of the EXDB,e.g., a Business Analytics Reporting (“BAR”) component. In someimplementations, the server may customize a business analytics report tothe attributes of a client of the user requesting the investmentstrategy analysis, e.g., 3401. The server may obtain an investmentstrategy analysis request from a client. The request may include detailsabout the client such as, but not limited to: client_type, client_IP,client_model, client_OS, app_installed_flag, and/or the like. The servermay parse the request, e.g., 3402, and determine the type of client(e.g., desktop computer, mobile device, smartphone, etc.). Based on thetype of client, the server may determine attributes of the businessanalytics report, including but not limited to: report size; reportresolution, media format, and/or the like, e.g., 3403. The server maygenerate the business analytics report according to the determinedattributes, e.g., 3404. The server may compile the report into a mediaformat according to the attributes of the client, e.g., 3405, andprovide the business analytics report for the client, e.g., 3406.Optionally, the server may initiate actions (e.g., generate a marketdata feed, trigger an investment action, trigger a wholesale purchase ofgoods for a retailer, etc.) based on the business analytics reportand/or data utilized in preparing the business analytics report, e.g.,3407.

Analytical Model Sharing

Thus, as seen from the discussion above, in various embodiments, theEXDB facilitates the creation of analytical models using which the dataaggregated by the Centralized Personal Information Platform of the EXDBmay be utilized to provide business or other intelligence to the varioususers of the EXDB. Examples of analytical models include the componentsdiscussed above in the discussion with reference to FIGS. 24 and 33A-B.In some implementations, the EXDB may facilitate the sharing of suchanalytical models among various users and/or other entities orcomponents associated with the EXDB. For example, a developer of ananalytical model such as the real-time offer merchant analyticsreport-generating component of FIG. 24 may distribute the analyticalmodel to other users of the EXDB. Optionally, the model may be describedaccording to an encryptmatics XML format, as discussed in detail furtherbelow in this disclosure. In some embodiments, the analytical model maybe provide without providing the model data sets based on which themodel was developed, so as to protect the privacy of the consumers whosedata were included in the model data set. In alternate embodiments, theEXDB may utilize a consumer data anonymization component such as thatdescribed above with reference to FIG. 32, before sharing the model dataset along with the analytical model.

FIG. 35 shows a logic flow diagram illustrating example aspects ofsharing an analytical model generated using data acquired using thecentralized personal information platform in some embodiments of theEXDB, e.g., an Analytical Model Sharing (“AMS”) component. In someembodiments, the EXDB may obtain an analytical model provided forsharing with other users, e.g., 3501. The EXDB may parse the analyticalmodel, e.g., using one of the parsers described below with reference toFIG. 49. The EXDB may, based on the parsing of the model, identify anymodel data set used to develop the analytical model, that is included inthe model provided for sharing, e.g., 3502. The EXDB may determine, ifsuch a model dataset is included, whether the model dataset includesprivate data that may be shared on an open exchange, e.g., personallyidentifiable information, e.g., 3503. If the data is allowed to beshared, e.g., 3504, option “No,” the EXDB may provide the analyticalmodel for sharing, e.g., to a model exchange website, without anyfurther processing, e.g., 3505. If however, the model dataset includedata that may not be shared, e.g., 3504, option “Yes,” the EXDB maydetermine whether the model dataset may be excluded from the model,while still allowing the model to be properly used, e.g., 3506. If themodel dataset is excludable, e.g., 3507, option “Yes,” the EXDB maydelete the model dataset from the analytical model, e.g., 3508, andprovide the modified analytical model for sharing, e.g., 3505. Ifhowever, the model dataset is not excludable, e.g., 3507, option “No,”the EXDB may determine whether the dataset can be anonymized while stillallowing the analytical model to function properly. If the dataset canbe anonymized, e.g., 3509, option “Yes,” the EXDB may anonymize themodel dataset, e.g., using the CDA component discussed above withreference to FIG. 32, e.g., 3510. Then, the EXDB may provide themodified analytical model including the anonymized dataset for sharing,e.g., 3505. If however, the model dataset cannot be anonymized, e.g.,3509, option “No,” the EXDB may generate and provide a sharing rejectionmessage to the provider of the analytical model for sharing, e.g., 3511.

Encryptmatics XML Data Converter

In some embodiments, the EXDB may utilize metadata (e.g., easilyconfigurable data) to drive an analytics and rule engine that mayconvert any structured data obtained via the centralized personalinformation platform, discussed above in this disclosure, into astandardized XML format (“encryptmatics” XML). See FIG. 14B foradditional description. The encryptmatics XML may then be processed byan encryptmatics engine of the EXDB that is capable of parsing,transforming and analyzing data to generate decisions based on theresults of the analysis. Accordingly, in some embodiments, the EXDB mayimplement a metadata-based interpretation engine that parses structureddata, including, but not limited to: web content, graph databases, microblogs, images or software code, and convert the structured data intocommands in the encryptmatics XML file format. The structured data mayinclude, without limitation, software code, images, free text,relational database queries, graph queries, sensory inputs, and/or thelike. As an example, the EXDB may convert software code written in SASintegrated system of software products provided by SAS Institute, Inc.,into a standard encryptmatics XML structure. The example below shows SAScode and encryptmatics XML that serves a similar purpose to the SAScode—defining a module's inputs, outputs, and function calls:

// SAS filename myFTL “my.378.FTL″; data MyFile; length yyddd $5. ;infile myFTL lrecl=50000; input @21 yyddd ; run; // Encryptmatics XML<lock name=″F: Transaction Date : yyddd″ inkeyid=″0″ inkeystart=“21″inkeystop=“25″ outkeyid=″31″ outkeyindex=″1″ function=″INSTANT″type=″STRING″ />

In the encryptmatics XML examples herein, a “key” represents acollection of data values. A “tumblar” represents a hash lookup tablethat may also allow wild card searches. A “lock” represents a definitionincluding one or more input data sources, data types for the inputsources, one or more data output storage variables, andfunctions/modules that may be called to process the input data from theinput data sources. A “door” may refer to a collection of locks, and avault may represent a model package defining the input, output, featuregeneration rules and analytical models. Thus, the encryptmatics XML maybe thought of as a framework for calling functions (e.g.,INSTANT—returns the raw value, LAG—return a key from a priortransaction, ADD—add two keys together, OCCURRENCE—returns the number oftimes a key value occurred in prior transactions, AVG—returns an averageof past and current key values, etc.) and data lookups with a sharedstorage space to process a grouped data stream.

In some embodiments, a metadata based interpretation engine may populatea data/command object (e.g., an encryptmatics XML data structuredefining a “vault”) based on a given data record, using configurablemetadata. The configurable metadata may define an action for a givenglyph or keyword contained within a data record. The EXDB may obtain thestructured data, and perform a standardization routine using thestructured data as input (e.g., including script commands, forillustration). For example, the EXDB may remove extra line breaks,spaces, tab spaces, etc. from the structured data. The EXDB maydetermine and load a metadata library, using which the EXDB may parsesubroutines or functions within the script, based on the metadata. Insome embodiments, the EXDB may pre-parse conditional statements based onthe metadata. The EXDB may also parse data to populate a data/commandobject based on the metadata and prior parsing. Upon finalizing thedata/command object, the EXDB may export the data/command object as XMLin standardized encryptmatics format. For example, the engine mayprocess the object to export its data structure as a collection ofencryptmatics vaults in a standard encryptmatics XML file format. Theencryptmatics XML file may then be processed to provide various featuresby an encryptmatics engine.

As an example, using such a metadata based interpretation engine, theEXDB can generate the encryptmatics XML code, provided below, from itsequivalent SAS code, provided beneath the encryptmatics XML codegenerated from it:

// SAS function code myInput = filename(“../data/30x. raw”, “fixed”,“../metaData/ftl_302.meta”); data myout;  set myInput; auth_amt =float(myInput.auth_amt); auth_amt2 = log(auth_amt);  run; proc freq data= myout;  tables auth_amt2 ;  run; // Equivalent encryptmatics XMLfunction code <init> loop=mainLoop <input>  keyname=myinput file=../data/30x.raw  format= fixed meta_data= ../metaData/ftl_302.meta</input> <output> keyname=myout file=VARRAY format=VARRAY meta_data={‘auth_amt2’: (1, 0, ‘String’), ‘auth_amt’: (0, 0, ‘String’)} </output></init> <vault> <door>  <lock>  outkey=myout  outkeyname=auth_amt inkey=myinput  inkeyname=auth_amt  function=float  type=String  </lock> <lock>  outkey=myout  outkeyname=auth_amt2  inkey=myout inkeyname=auth_amt  function=log  type=String  </lock> </door> </vault> <init> summary_level=2 loop=mainLoop <input>  keyname=myout file=VARRAY:myout  format=array meta_data= {‘auth_amt2’: (1, 0,‘String’), ‘auth_amt’: (0, 0, ‘String’)} </input> <output>keyname=_output file=stdout format=VARRAY meta_data= {‘_agg1’: (0, 0,‘object’)} </output> </init> <vault> <door>  <lock>  outkey=agg1 outkeyname=auth_amt2  inkey=myout  inkeyname=auth_amt2 function=instant  type=String  </lock> </door> <door>  <lock> outkey=_output  outkeyname=_agg1  function=aggfreq  type=object parser=noparse  groupkeyname=agg1  </lock> </door> </vault>

As another example, using such a metadata based interpretation engine,the EXDB can generate the encryptmatics XML code, provided below, fromits equivalent SAS code, provided beneath the encryptmatics XML codegenerated from it:

// SAS function code myInput = filename(“../data/vnd.test.json”, “JSON”,“../metaData/enrollment.meta”);  myTumblar =tumblarname(“../tumblars/enrollment.exp.tumblar”);  data myOut;  setmyInput;  customer_ipaddresstmp = tumble(myInput.customer_ipaddress ,customer_ipaddress );  customer_ipaddress =myOut.customer_ipaddresstmp/1000;  cvv_resulttmp =tumble(myInput.cv_result , cv_result );  cv_result =myOut.cvv_resulttmp/1000;  keep customer_ipaddress cv_result;  run; proc model data = myOut out=Scored; features = customer_ipaddresscv_result ; weights = 1,1 ; type = ‘bayes’ ;  run;  proc print data =Scored;  run; // Equivalent encryptmatics XML function code <init>loop=mainLoop <input>  keyname=myinput  file=../data/vnd.test.json format=JSON meta_data= ../metaData/ftl_302.meta <br/> </input> <output>keyname=myout file=VARRAY format=VARRAY meta_data= {‘cv_result’: (1, 0,‘String’), ‘customer_ipaddress’: (0, 0, ‘String’)} </output> <constant> indexname=_constant_1000  value=1000  type=float </constant> </init><vault> <door>  <lock>  outkey=_old_myout outkeyname=customer_ipaddresstmp  inkey=myinput inkeyname=customer_ipaddress  function=INSTANT  type=String tumblar=customer_ipaddress  </lock>  <lock>  outkey=_old_myout outkeyname=customer_ipaddress  inkey=_old_myout inkeyname=customer_ipaddresstmp  inkey2=_constants inkey2name=_constant_1000  function=divide  type=String  </lock> <lock>  outkey=_old_myout  outkeyname=cvv_resulttmp  inkey=myinput inkeyname=cv_result  function=INSTANT  type=String  tumblar=cv_result </lock>  <lock>  outkey=_old_myout  outkeyname=cv_result inkey=_old_myout  inkeyname=cvv_resulttmp  inkey2=_constants inkey2name=_constant_1000  function=divide  type=String  </lock> <lock>  outkey=myout  outkeyname=customer_ipaddress  inkey=_old_myout inkeyname=customer_ipaddress  function=instant  type=String  </lock> <lock>  outkey=myout  outkeyname=cv_result  inkey=_old_myout inkeyname=cv_result  function=instant  type=String  </lock> </door></vault>  <init> loop=mainLoop <input>  keyname=myout  file=VARRAY:myout format=array meta_data= {‘cv_result’: (1, 0, ‘String’),‘customer_ipaddress’: (0, 0, ‘String’)} </input> <output> keyname=scoredfile=VARRAY format=VARRAY meta_data= {‘_mdl1’: (0, 0, ‘float’)}</output> </init> <vault> <door>  <lock>  outkey=mdl1 outkeyname=customer_ipaddress  inkey=myout inkeyname=customer_ipaddress  function=instant  type=String  </lock> <lock>  outkey=mdl1  outkeyname=cv_result  inkey=myout inkeyname=cv_result  function=instant  type=String  </lock> </door><door>  <lock>  outkey=scored  outkeyname=_mdl1  function=_mdl1 type=float  fnc-weights=1.0,1.0  function=SUMPROB  model=bayes parser=noparse  groupkeyname=mdl1  </lock> </door> </vault>  <init>outputall=True loop=print <input>  keyname=scored  file=VARRAY:scored format=array meta_data= {‘_mdl1’: (0, 0, ‘float’)} </input> <output>keyname=_output file=stdout format=VARRAY meta_data= {‘_mdl1’: (0, 0,‘String’)} </output> </init> <vault> <door>  <lock>  outkey=_output outkeyname=_mdl1  inkey=scored  inkeyname=_mdl1  function=instant type=String  </lock> </door> </vault>

FIG. 36 shows a logic flow diagram illustrating example aspects of ametadata based interpretation engine of the EXDB that generatesstandardized encryptmatics XML from structured data obtained fromvarious sources via the centralized personal information platform, e.g.,an Encryptmatics XML Converter (“EXC”) component. In some embodiments,the EXDB may obtain a structured data object for conversion intoencryptmatics XML format, e.g., 3601. The EXDB may parse the structureddata, e.g., 3602. For example, the EXDB may utilize parsers such as theexample parsers discussed below with reference to FIG. 49. In someembodiments, the EXDB may determine and load a metadata library, usingwhich the EXDB may parse subroutines or functions within the script,based on the metadata. In some embodiments, the EXDB may pre-parseconditional statements based on the metadata. The EXDB may also parsedata to populate a data/command object based on the metadata and priorparsing. The EXDB may obtain the structured data, and perform astandardization routine using the structured data as input (e.g.,including script commands, for illustration). For example, the EXDB mayoptionally eliminate superfluous characters, e.g., extra line breaks,spaces, tabs, etc., to generate a modified structured data object, e.g.,3603. The EXDB may extract a glyph or keywords from the modifiedstructured data, e.g., 3604. The EXDB may, using the metadata library,lookup a database (e.g., a metadata library) for an encryptmatics XMLmetadata code snippet corresponding to the extracted glyph or keyword,e.g., 3605, and append the retrieved encryptmatics XML metadata to ametadata object, e.g., 3606. The EXDB may perform such a routine untilall glyphs or keywords are extracted and processed from the modifiedstructured data, see e.g., 3607. Then, the EXDB may, upon finalizing thedata/command object, export the data/command object as XML instandardized encryptmatics file format, e.g., 3608. For example, theengine may process the object to export its data structure as acollection of encryptmatics vaults in a standard encryptmatics XML fileformat. In some embodiments, the EXDB may execute an application definedby the exported encryptmatics file, e.g., on other structured dataavailable in the centralized personal information platform, e.g., 3609.

Thus, in some embodiments, the EXDB may gradually convert the entirecentralized personal information platform from structured data intostandardized encryptmatics XML format. The EXDB may also generatestructured data as an output from the execution of the standardizedencryptmatics XML application, and add the structured data to thecentralized personal information platform databases, e.g., 3610. In someembodiments, the EXDB may recursively provides structured data generatedas a result of execution of the encryptmatics XML application as inputinto the EXC component, e.g. 3611.

FIG. 37 shows a data flow diagram illustrating an example email dataaggregation procedure in some embodiments of the EXDB. In someimplementations, the pay network server may obtain a trigger to extractone or more monitorable user email addresseses and generate an emailaccess API query in order to monitor a user's email activity andaggregate the content thereof. For example, the pay network server mayperiodically perform an update of its aggregated database, e.g., 3710 a,with new information available from the user's email account activityoperating on the Internet. In one embodiment, the information aggregatedis the raw content of email messages, including header informationcontaining the server delivery path through which the message haspassed. As another example, a request for email data aggregation updatemay be obtained as a result of a user wishing to enroll in a service,for which the pay network server may facilitate data entry by providingan automated web form filling system using information about the userobtained from the email data aggregation update. In someimplementations, the pay network server may parse the trigger to extractaccess credentials with which to perform an email data aggregationupdate. The pay network server may generate a query for applicationprogramming interface (API) templates for various email providerservices (e.g., Gmail™, Yahoo Mail™, etc.) from which to collect emaildata for aggregation. In some embodiments, the aggregation templateswill be configured to provide access to the user's email account at theemail service provider. In other embodiments, the aggregation templateswill be configured to provide a mechanism to parse retrieved user emailinto a more suitable format for processing. In still other embodiments,the templates may indicate that an email transfer protocol (such as POP,IMAP, and/or the like) should be employed. In some instances, the emailtransfer protocol may be used over a secondary secured connection (suchas SSH, PGP, and/or the like).

In one embodiment, the pay network server may query, e.g., 3712, a paynetwork database, e.g., 3707, for email aggregation API templates forthe email provider services. For example, the pay network server mayutilize PHP/SQL commands similar to the examples provided above. Thedatabase may provide, e.g., 3713, a list of email access API templatesin response. Based on the list of API templates, the pay network servermay generate email aggregation requests, e.g., 3714. The pay networkserver may issue the generated email aggregation requests, e.g., 3715a-c, to the email network servers, e.g., 3701 a-c. For example, the paynetwork server may issue PHP commands to request the email providerservers for email data. An example listing of commands to issue emailaggregation data requests 3715 a-c, substantially in the form of PHPcommands, is provided below:

<?php $aggregated_email = “”; $mail = imap_open(‘{server.com:110/pop3}’,$user, $password); $headers = imap_headers($mail); for ($1=1;$i<=count($headers); $i++) { $structure = imap_fetchstructure($mail,$i); $structure_parts = $structure−>parts; $number_parts =count($structure_parts); for ($j=0; $j<=$number_parts; $j++) { $text =imap_fetchbody($mail,$i,$j); $aggregated_email .= nl2br(htmlspecialchars($text)).“<br>”;  } } ?>

In some embodiments, the email provider servers may query, e.g., 3717a-c, their databases, e.g., 3710 a-c, for email aggregation resultsfalling within the scope of the email aggregation request. In responseto the queries, the databases may provide email data, e.g., 3718 a-c, tothe email provider servers. The email provider servers may return theemail data obtained from the databases, e.g., 3719 a-c, to the paynetwork server making the email aggregation requests. An example listingof email data 3719 a-c, substantially in the form of JavaScript ObjectNotation (JSON)-formatted data, is provided below:

[“data”:[ {“headers”: “Delivered-To: MrSmith@gmail.com Received: by10.36.81.3 with SMTP1 id e3cs239nzb; Tue, 5 Mar 2020 15:11:47 -0800(PST) Return-Path: Received: from mail.emailprovider.com(mail.emailprovider.com [111.111.11.111]) by mx.gmail.com with SMTP idh19si826631rnb.2005.03.29.15.11.46; Tue, 5 Mar 2020 15:11:47 - 0800(PST) Message-ID: <20050329231145.62086.mail@mail.emailprovider.com>Received: from [11.11.111.111] by mail.emailprovider.com via HTTP; Tue,5 Mar 2020 15:11:45 PST Date: Tue, 5 Mar 2020 15:11:45 -0800 (PST) From:Mr Jones Subject: Dinner at Patsy's this weekend? To: Mr Smith”, “from_addr”: “MrJones@gmail.com”,  “from_name”: “Mr Jones”,  “to_addr”:“MrSmith@gmail.com”,  “subject”: “Dinner at Patsy's this weekend?” “date”: “Tue, 5 Mar 2020 15:11:45 -0800 (PST)”,  “msg_content”: “HeyFrank,\n\nWould you like to meet at Patsy's for dinner on Saturdaynight? Their chicken parm is as good as my mom's (and that's prettygood!).\n\nRafael” }, {  ... } ]]

In some embodiments, the pay network server may store the aggregatedemail data results, e.g., 3720, in an aggregated database, e.g., 3710 a.

FIG. 38 is a block diagram illustrating an example structure of adistributed linking node mesh, in one embodiment of the EXDB. In oneembodiment, the linking node mesh may be represented as a modified graphdata structure that contains nodes for entities and edges that representthe associations between the nodes. In one embodiment, the nodes areactual observable entities, such as a user 3801, a business 3803, anitem 3807, a review on an online web site, e.g., 3813, 3816, and/or thelike. The graph mesh may also contain deduced entities that have beeninserted by the EXDB as a result of the aggregation described herein,such as the aggregation of data from email, search queries, locationaggregation, and/or the like. Non-limiting examples of deduced entitynodes that may be inserted into the graph mesh include a deduced item,i.e., 3810 or a deduced opportunity, i.e., 3805. A deduced item may bean item that the mesh determines exists based on scanning emails, e.g.,3809, to determine that a concept that occurs with some frequency inemails is associated with a concept that is not yet linked into the meshgraph. An example deduced item may be a user's mother's chicken parmesan3810. In one embodiment, there may also be deduced opportunities addedto the mesh graph, e.g. 3805. A deduced opportunity may be anopportunity that is determined based on aggregated transaction data,e.g., 3804. For example, through the use of aggregated transaction datait may be determined by the EXDB that the price of a given set of itemsdeclines at a restaurant, e.g., 3803, during the week, e.g., 3805. Thisdecline in pricing may then allow the EXDB to determine that thereexists a special weekday menu with lower prices. In so doing, anopportunity for use by the mesh in providing recommendations orinformation to users may be created, e.g., 3805, in order to facilitatesearching the mesh.

In one embodiment, the mesh graph may also contain service items, e.g.,3807, such as a restaurants chicken parmesan or other menu item. Theservice item and its link to the business 3803, e.g., 3806, 3808, may bedetermined using a forward web crawl (such as by crawling from abusiness home page to its menu pages), or by a reverse web crawl, suchas by crawling using an Optical Character Recognition scanned menuforwarded through an email exchange and aggregated by an emailaggregating component of the EXDB.

In one embodiment, the mesh graph may additionally contain metaconcepts, e.g., 3810, 3812, 3815. Meta-concepts are conceptual nodesadded to the graph by EXDB that define not a specific entity (such as auser or a business) nor a specific deduced entity (such as a deduceditem or a deduced opportunity), but rather indicate an abstract conceptto which many more nodes may relate. For example, through web crawling,e.g., 3814, or email aggregation, e.g., 3817, user reviews may beimported as nodes within the mesh graph, e.g., 3813, 3816. Nodes may beanonymous, e.g., 3813, linked to a specific user's friend (such as toprovide specific user recommendations based on a social graph link),e.g., 3816, and/or the like. These reviews may be analyzed for positiveconcepts or words such as “delightful meal” or “highly recommended” andthereafter be determined by the EXDB to be a positive review and linkedto a mesh meta-concept of the kind positive review, e.g., 3815. In sodoing, the EXDB allows disparate aggregated inputs such as emailaggregation data, location aggregation data, web crawling or searchingaggregated data, and/or the like to be used to roll up concepts intoconceptual units.

In one embodiment, these conceptual meta concepts, e.g., 3815, may befurther linked to actual items, e.g., 3807. In so doing connections canbe formed between real world entities such as actual reviews of items,to meta-concepts such as a positive review or beneficial location, andfurther linked to actual items as a location. Further meta-concepts mayinclude activities such as dinner, e.g., 3812, a non-entity specificitem (e.g., not a restaurant's chicken parmesan and not a mother'schicken parmesan, but chicken parmesan as a concept), e.g., 3811. Theconnection of actual entity nodes with deduced entity nodes andmeta-concept nodes allows the mesh to answer a virtually limitlessnumber of questions regarding a given nodes connections and probableoutcomes of a decision.

In one embodiment, nodes within the mesh graph are connected by edgesthat have no magnitude. In another embodiment, the edges themselves mayhave meta-data associated with them that enable faster or betterquerying of the mesh. Example meta data that may be stored at a graphedge include a relative magnitude of connection between nodes, pathinformation regarding other nodes available from the edge, and/or thelike. In still other embodiments, intermediate or link nodes, e.g.,3804, 3806, 3808, 3814, 3817, 3809, may be inserted by the EXDB into themesh graph. These intermediate nodes may function as the equivalent ofan edge, in that they may describe a relationship between two nodes. Inone embodiment, the link nodes may contain information about the nodesthat they connect to. In so doing, the number of nodes in the graph thatneed to be searched in order to find a given type, magnitude or value ofconnection may be reduced logarithmically. Additionally, the link nodesmay contain data about how the relationship between the nodes it linkswas established, such as by indicating the connection was establishedvia search aggregation, email aggregation, and/or the like.

In one embodiment, the distributed linking node mesh may be stored in amodified open source database such as Neo4j, OrientDB, HyperGraphDB,and/or the like. An example structure substantially in the form of XMLsuitable for storing a distributed linking node mesh is:

<mesh> <nodes> <node id=”1” kind=”entity” type=”user”> <name=”John Doe”></node> <node id=”2” kind=”entity” type=”item”> <name=”iPhone” /></node> <node id=”3” kind=”deduced_entity” type=”business”> <name=”AppleComputer” /> <attribute type=”keyword” value=”iPhone” /> <deduced_fromvalue=”frequency_keyword” /> </node> <node> ... </node> </nodes><link_nodes> <linknode id=”78” in_node=”1” out_node=”3” value=”55” /><linknode id=”97” in_node=”1” out_node=”2” value=”124” /> ...</link_nodes> <edges> <edge from_node=”1” to node=”78” /> <edgefrom_node=”78” to node=”3” /> ... </edges> </mesh>

An example query suitable for querying a distributed linking node meshis:

START user=node(5,4,1,2,3) MATCH user-[:affinity]−>”iphone” WHEREentity.manufacturer =~ ′Apple.*′, link.strength >= 40 RETURN user,user.affinity

In another embodiment, an example query suitable for querying adistributed linking node mesh is:

##MODEL QUERY Language (JSON FORMAT) { 1: {‘LOWER’: 100, ‘BASETYPE’:[‘MODEL_001_001_00’, ‘MODEL_002_001_00’, ‘MODEL_003_001_00’,‘MODEL_004_001_00’] , ‘attribute’: ‘WEIGHT’, ‘rule’: ‘NEAR’, ‘OP’:‘PROX’, ‘type’: ‘TOKENENTITY’, ‘HIGHER’: 100} , 2: {‘type’: [‘USER’,‘MCC’], ‘rule’: ‘FOLLOW’} , 3: {‘rule’: ‘RESTRICTSUBTYPE’, ‘BASETYPE’:[‘MODEL_001_001_00’, ‘MODEL_002_001_00’, ‘MODEL_003_001_00’,‘MODEL_004_001_00’]}} }

FIGS. 39A-E are an example block diagram illustrating a distributedlinking node mesh search, in one embodiment, of the EXDB. The graphpresented in FIG. 39A-E is similar to the graph presented in FIG. 38 butnodes of different type are represented similarly for ease ofunderstanding. In one embodiment, a user 3901 may desire to find a gooddeal on dinner with friends at a restaurant nearby. The EXDB may beconfigured with a capability to extract sub-concepts from a natural formquery question, such as by natural language processing. Example toolssuitable for this type of processing may include OpenNLP, GraphExpression, FreeLing, and/or the like.

In one embodiment, the query portion relating to finding a good deal isperformed as a EXDB search to arrive arrive at a result of a deducedopportunity for lower prices during weekdays, e.g., 3902. The search maythen progress to extract the concept of a good deal merged with arestaurant nearby. Using an integrated location capability of a user'sdevice, the user's current location may additionally be provided to theEXDB for use in this portion of the query process, to produce a resultcontaining a deduced opportunity for lower prices (e.g., a “good deal”)at a business nearby wherein the lower prices are linked to the businessnearby with a certain degree of weight, e.g., 3903. In one embodiment,the search may progress to find results for the concept of a dinner(e.g., meta-concept dinner 3904), which is itself linked throughintermedia nodes to the business found in the previous portion of thesearch, e.g., 3905. In one embodiment, the search may then progress tofind connections that indicate that the user 3901 will like therestaurant, e.g., 3906, and that the user's friends will similarly likethe restaurant, e.g., 3907. The intermediate searches performed may bethen merged to produce a unitary result, e.g., 3908, for a restaurantmeeting the full criteria. In cases where no single entity meets all thecriteria, the most important criteria to a user may be first determinedusing its own EXDB search, such as a search that determines that a user3901 has never traveled to a nearby popular location area for dinner andtherefore concluding that location is very important to the user. In oneembodiment, multiple results 3908 may be returned and ranked foracceptability to both the user and his/her friends, enabling the user tothen choose a preferred location.

FIG. 39F shows an alternative embodiment of a distributed linking nodemesh search. Here, mesh user 3901 wants to determine the probabilitythat a user will buy Korean BBQ before a certain time, e.g., 3909. Thedistributed linking mesh may be queried. For example, the user'sprevious purchases of Korean BBQ (e.g., 3910, 3911), may be linked to ameta-concept that indicates an affinity for Korean BBQ, e.g., 3911. Theaffinity may be similarly linked to an entity indicating a purchasefrequency for Korean BBQ, e.g., 3912. Similarly, by aggregating datafrom the user's email correspondence (i.e., calendar updates, and/or thelike), the mesh may have an entity representing the user's scheduleincluding free time, e.g., 3913. Both the purchase frequency 3912 andthe user schedule 3913 may be linked to a mesh meta-concept of freetime, e.g., 3914, which may indicate that the entities are related whenthe user has free time (e.g., the individual may be more likely to gofor Korean BBQ when she is not working). By querying the distributedlinking node mesh for interrelations between entities built fromaggregation techniques (and deduced or input entities), a profile of theuser's future behavior may be similarly built. For example, if the userschedule indicates that the user is free on both Wednesday and Thursdayafternoons, and the aggregated purchase history indicates an affinity topurchase Korean BBQ both on those days (based on the purchasetransaction entities) and when the user is free (based on themeta-concept of free time), then a mesh search can return a probabilitybased on the respective weights of the constituent entity relationshipswith respect to the user.

FIGS. 40A-C are an example block diagram illustrating index creation ina distributed linking node mesh, in one embodiment of the EXDB. In oneembodiment, a non-indexed graph is exploded to form a chain ofrelationships from a single entity's perspective, e.g., 4001. A furthestnode graph traversal is then performed, e.g., 4002, whereby the linkingnodes are sequentially removed and replaced with a single edge which hasa magnitude of the connection between the two nodes, e.g., 4002 a, 4002b. A nearest node graph traversal may then be performed, e.g., 4003,whereby the magnitude of links further from the nearest node is modifiedby a factor of previous links. Modification proceed from nearest tofurthest nodes, e.g., 4003 a. In the example illustrated, a modificationis made to the second edge encountered to make its value as a relationof magnitude with User X incident on both the relationship of User X toBusiness Y and of Business Y to Deduced Opportunity L. This proceduremay produce a flattened graph from a single entity's perspective, e.g.,4004. The graph may then be further modified to a single perspectiveindexed graph, e.g., 4005, to reduce the number of hops in the graphfrom a given entity to any other entity within the indexed graph, so asto advantageously speed searching of the graph from the entity'sperspective, e.g., 4005 a. In one embodiment, the output of similarindexes performed from other entity perspectives, e.g., 4006 b, may belinked 4006 c to the generated perspective 4006 a. In so doing, theindex may form a graph that simultaneously allows easy searching fromthe perspective of a single entity while maintaining connection betweenentities of different perspectives.

FIG. 41 is an example block diagram illustrating aspects of anEncryptmatics XML converter component. In one embodiment, models may beinput in a number of disparate language, e.g., 4101. Languages mayinclude interpreted languages such as Python, PHP, and/or the like,e.g., 4102, intermediate compiled languages such as .Net, Java, and/orthe like, e.g., 4103, compiled languages such as C++, COBOL, and/or thelike, e.g., 4104. A user defined language may also be input, e.g., 4105.In one embodiment, the user defined language will be input with alanguage mapper, e.g., 4106 that defines a mapping of the user definedlanguage's functions, methods, supported types, and/or the like to alanguage known by the EXDB. In still other embodiments, a nativemeta-data language, e.g., 4107, may be input.

In one embodiment, languages other than a native meta-data language arepassed to a meta-data language conversion component 4108, such as anEncryptmatics XML converter. The converter may convert the language to ameta-data language 4109. In one embodiment, the meta data language maydescribe data sources 4110 including a private data store (not availableto the provided model), an anonymized data store that is based on theprivate data store (available to the provided model), and/or a publicdata store. In one embodiment, the meta-data language may be deconverted4111 to produce data queries and model logic 4112 that is parseable bythe EXDB interpreter.

FIG. 42 is an example logic flow illustrating input language loading byan Encryptmatics XML converter component, in one embodiment of a EXDB.In one embodiment, an input language definition is received, e.g., 4201.The language definition may be a file containing information about thefunctions available within the input language. In one embodiment, thelanguage definition is source code for the language to be loaded intothe EXDB. In still other embodiments, the language definition is anexecutable binary file suitable for the EXDB to issue sample commandsand receive output. In one embodiment, the current mesh languagedefinition may be retrieved 4202. The mesh language may be an XML basedmeta-data language that allows the description of data sources, datasource manipulations (both visible and not visible to the input model)and a model to be run against the data sources. A model may be a seriesof logic commands describing manipulations or conditional logic to applyto input or source data sources in order to reach a result. In oneembodiment, language loading may facilitate the user providing thedescription of data sources, data source manipulations, the model,and/or the like in a language with which the user is already familiar.The EXDB may then used a loaded language definition to convert thelanguage to a common meta-data based (e.g., XML based, JSON based,and/or the like) language with which to then parse and execute commandsfrom.

In one embodiment, the first unprocessed mesh language operation isextracted from the mesh language definition. An example operation may be“TRIM”, which may strip whitespace from the beginning and end of aninput string. A determination is made if the mesh operation has anequivalent operation in the input language, e.g., 4204. Such adetermination may be made by executing a sample command against theinput binary and observing the output to determine if an error occurred.In other embodiments, a publically available language definition website may be crawled to determine which function(s) within an inputlanguage likely map to the mesh operation equivalent(s). In someinstances, there will be a one-to-one mapping between the input languageand the meta-data based mesh language. If there is not a one-to-oneequivalence, e.g., 4205, a determination is made (using a proceduresimilar to that employed above) to determine if a combination of inputlanguage functions may equate to a mesh language operation, e.g., 4206.For example, an input language that supports both a left-trim (stripspace to left of string) and a right-trim operation (strip space toright of string) may be considered to support a mesh TRIM through acombination applying both the left-trim and right-trim operations,producing a substantially equivalent output result.

In one embodiment, if no matching combination is found, e.g., 4207, themesh operation may be marked as unavailable for the input language,e.g., 4208 and the next unprocessed mesh operation may then beconsidered. If a matching combination is found, e.g., 4207, an upperbound test may be employed to test the upper bound behavior of the inputlanguage operation and compare that to the upper bound behavior of anequivalent mesh operation, e.g., 4209. For example, some languages mayperform floating point rounding to a different degree of precision atupper bounds of input. By testing this case, a determination may be madeif the equivalent input language function will produce output equivalentto the mesh operation at upper bounds. In one embodiment, a lower boundtest may be employed to test the lower bound behavior of the inputlanguage operation and compare that to the lower bound behavior of anequivalent mesh operation, e.g., 4210. For example, some languages mayperform floating point rounding to a different degree of precision atlower bounds of input. By testing this case, a determination may be madeif the equivalent input language function will produce output equivalentto the mesh operation at upper bounds. In one embodiment, other customtests may then be performed that may be dependent on the mesh operationor the input language operation(s), e.g., 4211. If the results of thetest cases above produce output that is different than the expectedoutput for the equivalent mesh operation, e.g., 4212, an offset spanningfunction may be generated to span the difference between the languages.For example, in the example above if the rounding function in the inputlanguage is determined to produce different behavior than the equivalentmesh operation at a lower bound, a function may be provided in the inputor mesh language to modify any output of the given input languageoperations to create an equivalent mesh language operation output. Forexample, a given floating point number may be rounded to a given levelof significant digits to produce equivalent behavior.

In one embodiment, the offset spanning function may not be capable ofcompletely mapping the input language operation(s) to the mesh languageoperation, e.g., 4214. In one embodiment, previous versions of the meshlanguage definition, e.g., 4215, may then be tested using a proceduresubstantially similar to that described above to determine if they maycompletely map the input language, e.g., 4216. If the previous versionof the mesh language definition completely maps the input language, themesh language definition version for the input language may be set tothe previous version, e.g., 4217. For example, a previous version of themesh language definition may contain different capabilities or functionbehaviors that allow it to completely map to an input language. Ifprevious versions of the mesh input language do not completely map tothe input language, language clipping parameters may be generated, e.g.,4218. Language clipping parameters are input limitations that are placedon an input language such that any inputs within the input limitationsrange will produce compliant mesh operation output. Inputs outside thatrange may generate an error. In one embodiment, language clippingparameters may include limits to the upper bound or lower bound ofacceptable input. Such limits may be determined by iteratively testingincreasing or decreasing inputs in order to find an input range thatmaps completely to the mesh operation.

In one embodiment, the current mesh operation, input languageoperation(s) any spanning functions or language clipping parameters, themesh language version, and/or the like may be stored in an inputlanguage definition database, e.g., 4219. If there are more unprocessedmesh language operations, e.g., 4220, the procedure may repeat.

FIGS. 43A-B show an example logic flow for input model conversion, inone embodiment of an EXDB. In one embodiment, a language command file isreceived, e.g., 4301. The language command file may contain instructionsin any language which has been loaded into the EXDB (e.g., FIG. 42). Theinput language command file may contain instructions that may describe aset of manipulations that may be performed on a data set (e.g., a dataset that is input as part of the input language command file, a data setthat is loaded from an external data source, and/or the like). In oneembodiment, input language definitions corresponding to the language ofthe input language command file is retrieved, e.g., 4302. A meshlanguage definition, which may specify operations that are availablewithin the mesh language, may also be retrieved, e.g., 4303.Non-conditional logic flow blocks in the input language command file maybe determined, e.g., 4304. A non-conditional logic block represents theoutermost logic contained within an input language command file. Forexample, if a file contains no conditional logic (i.e., no if/than/elseblocks, and/or the like), then the outermost logic may be the completeset of input language commands themselves. In one embodiment, a runblock is created for each outermost non-conditional logic flow block.The metadata run blocks are then populated with logic commands furtherin the procedure. In one embodiment, any variables that are initializedwithin the logic block corresponding to the run block are determined,e.g., 4306. A variable initialization template may then be determined,e.g., 4307. In one embodiment, the input language definition is used todetermine if an equivalent meta-data based variable type is available inthe mesh language definition for each of the variables initialized inthe input language command file, e.g., 4308. If all variable types arenot available, a model input error may be raised, e.g., 4309.

In one embodiment, the variable initialization template and the inputlanguage definition are used to create a constants block based on thevariable initialization template, e.g., 4310. Within the constantsblock, any constants that were included in the input language commandfile may be stored as structured XML. An example constants block,substantially the form of XML is as follows:

<constant> indexname=”0” value=’row by row’ Type=”string” </constant>

In one embodiment, there may be multiple constant blocks definedcorresponding to multiple constant values in the input language commandfile. In other embodiments, constants may be collapsed to one block.

In one embodiment, the input datasources may then be determined based onthe input language command file, e.g., 4311. For example, an inputdatasource may be defined directly in the input language command file(such as by declaring a variable as an array to values in the inputlanguage command file). In other embodiments, the inputs may be externalto the input language command file, such as a third party library orloaded from an external source file (such as a comma delimited file, viaa SQL query to an ODBC compliant database, and/or the like). A meshlanguage input datasource template may then be retrieved, e.g., 4312, toprovide a structure to the EXDB to use in formatting the inputs asmeta-data. The datasources may be scanned to determine if they areavailable to the model (such as by executing “ls−l” on a POSIX compliantUnix system), e.g., 4313. If the datasources are available to the model,then a meta data language input block may be created using the inputdatasource template, the language definition, and the input languagecommand file, e.g., 4314. An example input block substantially in theform of XML is:

<input> keyname=”test_by” file=”<ecyptmatics install>/test_by.egd”format=”ecdataformat” meta_data={‘col8’: (7, 0, ‘string’),‘_(——)header’: True, ‘col_2’: (1,0,’int’), ‘col_3’: (2,0,’int’),‘col_1’: (0,0,’int’), ‘col_6’: (5,0,’julian’), ‘col_7’: (6,0,’float’),‘col_4’: (3,0,’ordinaldate’), ‘col_5’: (4,0,’date’)} </input>

In one embodiment, a mesh language output template is determined, e.g.,4315 and an output block is created using a procedure substantiallysimilar to that described above with respect to the constant and inputblocks, e.g., 4316. An example output block, substantially in the formof XML is:

<output> keyname=“myout” file=“stdout” format=“deliminated”meta_data={‘col_2’: (2, 0, ‘String’), ‘col_2_l’: (4, 0, ‘String’),‘test’: (0, 0, ‘String’), ‘col_3’: (3, 0, ‘String’), ‘col_1’: (1, 0,‘String’), ‘sum_col_7’: (5, 0, ‘String’)} deliminator= “csv” </output>

In one embodiment, the constant block, input block, and output block areadded to a newly created initialization block and the initializationblock is added to the current run block, e.g., 4317. An example runblock with a complete initialization block included therein,substantially in the form of XML is as follows:

<run> <init> processor=process <input>  keyname=“test_by” file=“<ecryptmatics install>/test/data/test_by.egd” format=“ecdataformat”  deliminator=“csv” meta_data={‘col_8’: (7, 0,‘string’), ‘_(——)header’: True, ‘col_2’: (1, 0, ‘int’), ‘col_3’: (2, 0,‘int’), ‘col_1’: (0, 0, ‘int’), ‘col_6’: (5, 0, ‘julian'), ‘col_7’: (6,0, ‘float’), ‘col_4’: (3, 0, ‘ordinaldate’), ‘col_5’: (4, 0, ‘date’)}</input> <output> keyname=“myout” file=“stdout” format=“deliminated”meta_data={‘col_2’: (2, 0, ‘String’), ‘col_2_l’: (4, 0, ‘String’),‘test’: (0, 0, ‘String’), ‘col_3’: (3, 0, ‘String’), ‘col_1’: (1, 0,‘String’), ‘sum_col_7’: (5, 0, ‘String’)} deliminator= “csv” </output><constant>  indexname=“0”  value=‘row by row’  type=“string” </constant></init> </run>

In one embodiment, a vault block will then be created, e.g., 4318. Alogic command block will be extracted from the input logic command file,e.g., 4319. A logic command block is a logic block that is anon-outermost non-conditional logic flow. A door block may then be addedto the vault block, e.g., 4320. A logic command, representing a discretelogic operation, may then be extracted from the logic command block,e.g., 4321. The logic command may be a tumbler, e.g., 4322, in whichcase a tumbler key may be looked up in a tumbler database and thetumbler may be processed, e.g., 4323. Further detail with respect totumbler processing may be found with respect to FIGS. 44-45. The logiccommand may then be mapped to a mesh language equivalent by using thelanguage definition file, e.g., 4324. A mesh template logic commandtemplate, containing formatting information for a logic command, may beretrieved, e.g., 4325. In one embodiment, a lock block may be createdusing the mesh language definition, the language definition, and thelogic command, e.g., 4326. The created lock block may be added to thecurrent door block, e.g., 4327. In one embodiment, if there are morelogic commands, e.g., 4328, the procedure may continue. If there aremore logic command blocks, e.g., 4329, the procedure may similarlycontinue. In one embodiment, if there are more outermost non-conditionallogic flow blocks in the input language command file, e.g., 4330, theprocedure may continue with respect to FIG. 43A.

FIG. 44 is an example block diagram illustrating a tumbler data sourcemanipulation and anonymization component, e.g., a TDS Component. In oneembodiment, a user model may call a tumbler as part of a logic commandblock processing (e.g., in order to perform a hash table lookup, toprovide third-party data, to import anonymized transaction data, and/orthe like). In one embodiment, portions of the data manipulation may notbe visible, e.g., 4401, to the user model in order to maintain privacyfor the record owners, to preserve business secrets, and/or the like. Inone embodiment, the data source to be anonymized is loaded into akey/value table, e.g., 4402. The entire matrix may be considered as atumblar key. In other embodiments, a single cell within the matrix maybe a tumblar key. In still other embodiments, the matrix may take theform of a n×n matrix of arbitrary size (e.g., a 4×4×4×4 matrix, and/orthe like) In one embodiment, the keys or values may be pointers tounderlying data records. In another embodiment, the keys or values maythemselves be the data for manipulation. Commands may be read from thetumblar file (which may, in some embodiments, have a formatsubstantially similar to an input language command file, e.g., 4301).The commands may change some values in the matrix to other values, suchas may be done to anonymize user payment card information, e.g., 4403.In other embodiments, data may be removed from the matrix and replacedwith other data values, e.g., 4404. When indicated by the tumblar file,when a set number such as 5 anonymization operations have beenperformed, or when the tumblar key has reached a certain value, thetumblar key may be considered visible to the user model, e.g., 4405. Inso doing, the current keychain may be visible to the user model, e.g.,4407. Additional operations may then be performed on the key, extendingthe keychain, e.g., 4408. A keychain is a representation of current andpast values of a key/value store. In one embodiment, the keychain 4409may be returned. The keychain may contain an n×n sized matrix (i.e., asingle 2D matrix, a 3D collection of 2D matrix, a 4D matrix, and/or thelike), e.g., 4409 a, 4409 b.

In one embodiment, a tumblar file may be substantially in the form ofXML as follows:

<xml> <run> <init> processor=process tumblar_name=Nonetumblar_path=c:\1m\Ecryptmatics\ecryptmatics\test\tumblars\flaretumblarkey=flare <input>  keyname=“flares”  file=“<ecryptmaticsinstall>/test/data/flare.data1”  format=“deliminated”  deliminator=“ ”meta_data={‘evolution’: (4, 0, ‘Evolution’), ‘prev_activity’: (5, 0,‘Previous 24 hour flare activity code’), ‘area’: (8, 0, ‘Area’),‘are_largest_spot’: (9, 0, ‘Area of the largest spot’),‘histocially_complex’: (6, 0, ‘Historically- complex’), ‘complex’: (7,0, “Did region become historically complex on this pass across the sun'sdisk”), ‘class_cd’: (0, 0, ‘Code for class (modified Zurich class)’),‘activity’: (3, 0, ‘Activity’), ‘spot_dict_cd’: (2, 0, ‘Code for spotdistribution’), ‘largets_spot_cd’: (1, 0, ‘Code for largest spot size’),‘_(——)header’: False} </input> <output> keyname=“myout” file=“stdout”format=“deliminated” meta_data={‘evolution’: (3, 0, ‘String’), ‘area’:(1, 0, ‘String’), ‘complex’: (2, 0, ‘String’), ‘activity’: (0, 0,‘String’)} deliminator= “csv” </output> </init> <vault> <door> <lock> outkey=“myout”  outkeyname=“activity”  inkey=“flares” inkeyname=“activity”  function=“tumble”  type=“String” tumblar-masks=“*”  fnc-tumblar-key-table=“flare.activity” tumblar-default=“None” </lock> <lock>  outkey=“myout”  outkeyname=“area”  inkey=“flares”  inkeyname=“area”  function=“tumble” type=“String”  tumblar-masks=“*”  fnc-tumblar-key-table=“flare.area” tumblar-default=“None” </lock> <lock>  outkey=“myout” outkeyname=“complex”  inkey=“flares”  inkeyname=“complex” function=“tumble”  type=“String”  tumblar-masks=“*” fnc-tumblar-key-table=“flare.complex”  tumblar-default=“None” </lock><lock>  outkey=“myout”  outkeyname=“evolution”  inkey=“flares” inkeyname=“evolution”  function=“tumble”  type=“String” tumblar-masks=“*”  fnc-tumblar-key-table=“flare.evolution” tumblar-default=“None” </lock> </door> </vault> </run> </xml>

FIG. 45 is an example logic flow showing a tumblar data sourceanonymization component, e.g., a TDS component, in one embodiment of aEXDB. In one embodiment, a user unaccessible data source request and auser generated model containing tumblar data source manipulations may bereceived, e.g., 4501. In one embodiment, a tumblar key may be extracted,e.g., 4502. A tumblar definition corresponding to the tumblar key may beretrieved from a tumblar database, e.g., 4503. A tumblar definition maycontain manipulations (e.g., functions, methods, and/or the like) thatmay be performed on a given source file before the data is madeavailable for use in a user model. In one embodiment, an input/startingkey name may be determined (e.g., by inspecting an init block or byinspecting the input key values in the first lock of the first door ofthe first vault in the first run block in the tumblar file), e.g., 4505.An unprocessed internal tumblar data operation may be extractedincluding an input and an output key, e.g., 4506. An internal tumblaroperation may be an operation that is performed before a user model hasaccess to the data store, such as data manipulations that anonymizedata. Manipulation operations may include bit shifting, replacing ormasking certain field values in the data set, swapping data valuesbetween records (such as may be done to maintain a total of all valuesor the average of all values while not revealing to the user model theunderlying data). In one embodiment, the current map located at theinput key may be duplicated and stored, e.g., 4507. The operation maythen be performed on the data copy, e.g., 4508. In so doing, a chain(e.g., a key chain) of values may be created for a single data point. Ifthe current output key is visible to the user model (such as if theoutput key is >a given value such as 31, the output has undergone agiven number of operations, and/or the like), e.g., 4509, then any mapsequal to or greater than the current map may be marked as visible to theuser model, e.g., 4510. Manipulation operations may continue on the dataand an unprocessed external tumblar data operation (e.g., an operationvisible to the user model) may be extracted, e.g., 4511. The current mapmay be duplicated, e.g., 4512, and stored as a new map also visible tothe user model, e.g., 4512. In one embodiment, the external tumblar dataoperation may then be applied to the copied map, e.g., 4513. If thereare no more processed external tumblar data operations, e.g., 4514, theuser model visible portion of the keychain may be returned, e.g., 4515.

FIG. 46 is an example data flow illustrating mesh aggregation andcluster querying, in one embodiment of a EXDB. In one embodiment, afirehose server 4601 provides firehose input, e.g., 4602 to a meshserver 4603. A firehose server may be a server capable of acceptinginput from one or more data sources (e.g., Twitter or Facebook posts,transaction records, email data, and/or the like) at a high relativeflow rate. In one embodiment, the firehose server may perform somemanipulations on the received data before it is input to the mesh server4603. An example firehose input 4602, substantially in the form of XMLformatted data is:

<firehose_input>  <input type=”email” id=”1”> <dictionary_entry>  {id:“1h65323765gtyuf#uy76355”, type: email, category: {cat1: “food”, cat2:“dinner”}, from_addr : “john.doe@gmail.com”, to_addr:“jane.doe@gmail.com”, subject: “Korean BBQ this weekend?”,dictionary_keywords: “Korean, dinner, nyc”, content_hash:“7m865323476feeaniiji”} </dictionary_entry> <datetime>Jan 20, 202015:23:43 UTC</datetime> <from_addr>john.doe@gmail.com</from_addr><to_addr>jane.doe@gmail.com</to_addr> <subject>Korean BBQ thisweekend?</subject> <content> Received: by 10.36.81.3 with SMTP1 ide3cs239nzb; Tue, 5 Mar 2020 15:11:47 -0800 (PST) Return-Path: Received:from mail.emailprovider.com (mail.emailprovider.com [111.111.11.111]) bymx.gmail.com with SMTP id h19si826631rnb.2005.03.29.15.11.46; Tue, 5 Mar2020 15:11:47 - 0800 (PST) Message-ID:<20050329231145.62086.mail@mail.emailprovider.com> Received: from[11.11.111.111] by mail.emailprovider.com via HTTP; Tue, 5 Mar 202015:11:45 PST Date: Tue, 5 Mar 2020 15:11:45 −0800 (PST) From: John Doe<john.doe@gmail.com> Subject: Korean BBQ this weekend? To: Jane Doe<jane.doe@gmail.com> Hi Jane, Would you like to meet up in New York citythis weekend for Korean BBQ? I know this great place down on SpringStreet. John </content>  </input>  <input type=”tweet” id=”2”> ... </input>  <input type=”purchase_transaction” id=”3”> ...  </input> <input type=”web_search” id=”4”> ...  </input>  <input id=”n”> ... </input> </firehost_input>

In one embodiment, the mesh structure may then be updated, e.g., 4604.Further detail regarding updating the mesh structure can be foundthroughout this specification, drawing and claims, and particularly withreference to FIGS. 15-19. In one embodiment, a clustering node 4605 maysend a cluster categories request 4606 to the mesh server. A clustercategories request may contain a category or deduced concept that is tobe added to the mesh. In one embodiment, the category may have nopre-existing associations in the mesh (e.g., the category to be addedmay be an orphan category). An example cluster categories request 4606,substantially in the form of an HTTP(S) POST message including XML is:

POST /cluster_categories.php HTTP/1.1 Host: www.meshserver.comContent-Type: Application/XML Content-Length: 667 <?XML version = ″1.0″encoding = ″UTF-8″?> <cluster_categories_request> <clusteroperation=”add”> <concept value=”iphone” /> <concept_related_conceptvalue=”apple” /> <concept_keyword value=”64GB” /> <concept_keywordvalue=”Steve Jobs” /> </cluster> <cluster> ... </cluster></cluster_categories_request>

In an alternative embodiment, an example cluster categories request4606, substantially in the form of an HTTP(S) POST message including XMLis:

POST /cluster_categories.php HTTP/1.1 Host: www.meshserver.comContent-Type: Application/XML Content-Length: 667 <?XML version = ″1.0″encoding = ″UTF-8″?> <cluster_categories_request><cluster_operation=”add”> <concept value=”portable music player” /><manufacturer>Apple Computer</manufacturer> <model>iPod Touch32GB</model> <size>32GB</size> </cluster> <cluster> ... </cluster></cluster_categories_request>

In one embodiment, the cluster categories request above may be modifiedby the EXDB as a result of aggregated data. For example, a request tocreate a cluster for an iPod of a given size may be supplemented withalternative models/sizes. In so doing, the mesh may expand arecommendation, graph entity, and/or the like to encompass concepts thatare connected with the primary concept. In one embodiment, this modifiedcluster may take the form a the form of XML substantially similar to:

<cluster> <concept value=”portable music player” /> <manufacturer>AppleComputer</manufacturer> <model> <1>iPod Touch 32GB</1> <2>iPod Touch64GB</2> <3>iPod Touch 128GB</3> <4>iPhone 32GB</4> <5>iPhone 64GB</5><6>iPhone 128GB</6> </model> <size>32GB OR 64GB OR 128GB</size></cluster>

In one embodiment, the mesh structure may be updated in response to thecluster categories request, e.g., 4604. In one embodiment, a user 4607may use his/her mobile device to indicate that they wish to purchase anitem based on cluster concepts, e.g., a user bid/buy input 4608. Forexample, a user may query “I want the TV that AV Geeks thinks is bestand I'll pay $1,500 for it”. In one embodiment, the query may besubstantially in the form of a language input such as the above, whichmay be parsed using natural language processing packages such asFreeLing, LingPipe, OpenNLP, and/or the like. In other embodiments, theuser may be presented with a structured query interface on their mobiledevice that allows a restricted set of options and values from which tobuild a bid/buy input 4608. For example, a user may be given a list ofcategories (such as may be built by querying a categories database asdescribed with respect to FIG. 49) from which to choose when making abid/buy input. In one embodiment, a clustering server 4609 may receivethe user bid/buy input 4608 and generate a consumer cluster based bidrequest, e.g., 4610 and provide same to a clustering node 4605. Anexample consumer cluster based bid request 4610, substantially in theform of an HTTP(S) POST message including XML is:

POST /consumer_bid_request.php HTTP/1.1 Host: www.meshserver.comContent-Type: Application/XML Content-Length: 667 <?XML version = ″1.0″encoding = ″UTF-8″?> <consumer_cluster_based_bid_request> <datetime>Jan21, 2020 5:34:09 UTC</datetime> <user_id>43246</user_id> <request><type>bid</type> <item> <item_query>LCD Television</item_query><type_desired value=”best” /> <cluster_source value=”AV Geeks.com” /><cluster_min_expertise_level value=”top2prct” /> <max_pricevalue=”1500.00” currency=”USD” /> <expire_request value=”30days” /><payment type=”credit”> <card_type>VISA</card_type><card_num>98765436598766732</card_num> <card_exp>0525</card_exp></payment> <shipping_address> <addr1>100 Main St.</addr1><city>Anytown</city> <state>CA</state> <zip>90145</zip></shipping_address> </item> </request></consumer_cluster_based_bid_request>

In an alternative embodiment, the consumer cluster based bid request maybe generated using the user interface described herein and with respectto FIG. 48A-B. In one embodiment, the consumer cluster based bid request4610, generated using the interface may be substantially in the form ofan HTTP(S) POST message including XML:

POST /consumer_bid_request.php HTTP/1.1 Host: www.meshserver.comContent-Type: Application/XML Content-Length: 667 <?XML version = ″1.0″encoding = ″UTF-8″?> <consumer_cluster_based_bid_request> <datetime>Jan21, 2020 5:34:09 UTC</datetime> <user_id>43246</user_id> <request><type>bid</type> <item> <item_query>headphones</item_query> <quantityvalue=”2” /> <requirement value=”rated_top_3” /> <cluster_sourcevalue=”consumerreports.com” /> <max_price value=”249.95” currency=”USD”/> <expire_request value=”January 15, 2020” /> <payment type=”credit”><card_type>VISA</card_type> <card_num>98765436598766732</card_num><card_exp>0525</card_exp> </payment> <shipping_address> <addr1>100 MainSt.</addr1> <city>Anytown</city> <state>CA</state> <zip>90145</zip></shipping_address> </item> </request></consumer_cluster_based_bid_request>

In one embodiment, in response to the consumer cluster based bid request4610, the clustering node 4605 may generate a cluster request 4611. Acluster request may be a request to search the mesh in order to findresults (e.g., items matching a cluster's buying habits, merchantsoffering an item, alternative items for purchase, friends that havealready purchased items, items the user already owns—based on, forexample, past purchase transactions—that may satisfy the request, and/orthe like). An example query suitable for querying a distributed linkingnode mesh is:

START user=node(5,4,1,2,3) MATCH entity-[:affinity]−>”consumer_reports”WHERE entity.recommended >= ′3′, entity.recommendation.item.type ~=“headphones” RETURN entity.recommendation.item.name,entity.recommendation.item.model,entity.recommendation.item.averageprice

In one embodiment, the mesh server may provide a cluster requestresponse 4612. An example cluster request response 4612 substantially inthe form of an HTTP(S) POST message including XML is:

POST /cluster_request_response.php HTTP/1.1 Host: www.clusteringnode.comContent-Type: Application/XML Content-Length: 667 <?XML version = ″1.0″encoding = ″UTF-8″?> <cluster_request_response> <requested_item><item_query>LCD Television</item_query> <type_desired value=”best” /><cluster_source value=”AV Geeks.com” /> <cluster_min_expertise_levelvalue=”top2prct” /> <max_price value=”1500.00” currency=”USD” /></requested_item> <cluster_results><num_users_meeting_cluster_value=”2541” /><average_user_feedback_ranking value=”94%” /> <cluster_user_purchases><item rank=”1”> <desc>Sony Bravada 50” LCD 645</desc><model>KDL50EX645</model> </item> <item rank=”2”> <desc>Sony Bravada 50”LCD 655</desc> <model>KDL50EX655</model> </item> <item> ... </item></cluster_user_purchases> </cluster_results> </cluster_request_response>

In an alternative embodiment, an example cluster request response 4612substantially in the form of an HTTP(S) POST message including XML is:

POST /cluster_request_response.php HTTP/1.1 Host: www.clusteringnode.comContent-Type: Application/XML Content-Length: 667 <?XML version = ″1.0″encoding = ″UTF-8″?> <cluster_request_response> <requested_item><item_query>headphones</item_query> <quantity value=”2” /> <requirementvalue=”rated_top_3” /> <cluster_source value=”consumerreports.com” /><max_price value=”249.95” currency=”USD” /> <expire_requestvalue=”January 15, 2020” /> </requested_item> <cluster_results><cluster_consumer_reports_ranking> <item consumer_reports_rank=”1”><desc>Panasonic Technics Pro DJ</desc> <model>RP-DH1250</model><avg_price>$235.55</avg_price> </item> <item consumer_reports_rank=”2”><desc>Coby In Ear Headphones</desc> <model>CVEM76PNK</model><avg_price>$245.55</avg_price> </item> <item consumer_reports_rank=”3”><desc>Shure E2c-n Sound Isolating Earphones</desc> <model>SHE2CN</model><avg_price>$249.95</avg_price> </item></cluster_consumer_reports_ranking> </cluster_results></cluster_request_response>

In one embodiment, the clustering node 4605 may then process the clusterresponse and create transaction triggers. Further details regardingcluster request response 4612 processing may be found throughout thespecification, drawings and claims and particularly with reference toFIG. 47, e.g., a CRA Component.

In one embodiment, a lead cluster order request may be generated formerchants that were identified as a result of the cluster responseanalysis, e.g., 4613. In other embodiments, a default list of merchantsmay be used. A lead cluster order request may contain informationrelating to the identified purchase that the user 4607 wishes to engagein. In the example above, for example, the analysis may have determinedthat based on the aggregated AV Geeks user expert preferenceinformation, the user should purchase Sony television model KDL50EX645or KDL50EX655. The analysis may also have determined that a givenmerchant sells those models of television (such as by using aggregatedsales transaction data as described herein). A request may then be sentto the merchant indicating a purchase item, a user lead that may executethe purchase and a price the user is willing to pay. In one embodiment,the user identity is not provided or is anonymized such that themerchant does not have information sufficient to determine the actualidentity of the user but may determine if they wish to execute the saleto the user. An example lead cluster order request 4614, substantiallyin the form of an HTTP(S) POST message containing XML data:

POST /lead_cluster_order_request.php HTTP/1.1 Host:www.merchantserver.com Content-Type: Application/XML Content-Length: 667<?XML version = ″1.0″ encoding = ″UTF-8″?> <lead_cluster_order_request><lead validFor=”30_days”> <type>television</type> <items join=”OR”><item model=”KDL50EX645” /> <item model=”KDL50EX655” /> </items><user_information> <name>John Doe</name><email>john.doe@gmail.com</email> <phone>865-765-3465</phone></user_information> <payment type=”credit”> <card_type>VISA</card_type><card_num>98765436598766732</card_num> <card_exp>0525</card_exp></payment> <shipping_address> <addr1>100 Main St.</addr1><city>Anytown</city> <state>CA</state> <zip>90145</zip></shipping_address> </lead> <lead> ... </lead></lead_cluster_order_request>

In one embodiment, a merchant may accept the order and generate a leadcluster order accept/reject response. In other embodiments, the merchantmay indicate that they wish to hold the lead opportunity open and mayaccept at a later time if no other merchant has filled the lead clusterorder request. In still other embodiments, the merchant response maycontain a counteroffer for the user (e.g., $1600), which the user maythen accept or decline. In one embodiment, the user receives an orderacceptance confirmation 4617 indicating that their order has beenfulfilled.

In one embodiment, a user may cancel a cluster based bid request priorto the merchant fulfilling the order. For example, a user may transmit auser cancel input 4618 to clustering server 4609. The clustering servermay forward the cancel message to the clustering node 4605, e.g., 4619,which may in turn forward the cancel message to the merchant(s) server4615, e.g., 4620.

FIG. 47 is an example logic flow illustrating cluster response analysisand transaction triggering, e.g., a CRA component, in one embodiment ofa EXDB. In one embodiment, a cluster request response is received, e.g.,4701. Cluster criteria (i.e., user requesting cluster, the criteria forthe cluster, payment/shipping information for the user purchase bid,and/or the like) may be extracted from the cluster request response,e.g., 4702. In one embodiment, the cluster criteria is examined todetermined if it meets the minimum cluster criteria, e.g., 4703.Examples of minimum cluster criteria include minimum feedback ranking ofusers in cluster, minimum years of expertise of users in cluster, medianvalue of items returned, and/or the like. If the cluster criteria is notgreater than the minimum cluster criteria, the user may be prompted toadjust the minimum criteria and a search may be re-run, e.g., 4704. Inother embodiments, the criteria may be adjusted automatically by theEXDB or a third-party database may be queried to determine new minimumcriteria (e.g., a user expertise ranking service, a user review site,and/or the like).

In one embodiment, candidate purchase items may be extracted from thecluster request response, e.g., 4705. A merchant database may be queriedto determine merchants selling the candidate purchase items. An examplemerchant database query, substantially in the form of PHP/SQL commandsis provided below:

<?PHP header(‘Content-Type: text/plain’);mysql_connect(“localhost”,$DBserver,$password);mysql_select_db(“merchants.sql”); $query = “SELECT merchant_id,merchant_name, price, quantity_on_hand FROM merchants WHEREmerchant_item_id LIKE ‘%’ $cluster_returned_model_num”; $result =mysql_query($query); // perform the search querymysql_close(“merchants.sql”); // close database access ?>

In one embodiment, a maximum price the user is willing to pay isdetermined, e.g., 4707. An average selling price of the candidatepurchase items may be determine (such as by querying a merchant tablecontaining price history, querying a price history table, performing alive crawl of a merchant's web site, and/or the like). If the user'smaximum price is not within a given range of the average merchant itemprice, e.g., 4709, a price trend database may be queried, e.g., 4710. Aprice trend database may contain historical information relating to theprice of an item over time. If the price trend (i.e., the linearextrapolation of the historical prices, and/or the like) shows that theaverage price of the item will be within 40% of the user's maximum pricebefore the user purchase bid expires, e.g., 4711, the user purchase bidrequest may be held, e.g., 4712, so that the cluster response analysismay be re-run again before the bid expires. In another embodiment, evenif the user's price will not be within a range of the average price ofan item at the queried merchants, the user procedure may continue if theuser has been marked as a high priority bid user (e.g., a frequentbidder, a new bidder, and/or the like), e.g., 4713. In one embodiment,the first merchant that has stock of the item may be selected, e.g.,4714. If the merchant has received greater than a set amount of bids ina time period, e.g., 4715, another merchant may be selected. In sodoing, one merchant may not be overwhelmed with bids. In one embodiment,a lead cluster order request is created and transmitted to the merchant,e.g., 4716.

FIGS. 48A-C show user interface diagrams illustrating example aspects ofa discovery shopping mode of a virtual wallet application in someembodiments of the EXDB. In some embodiments, the virtual walletapplication may provide a ‘discovery shopping’ mode for the user. Forexample, the virtual wallet application may obtain information onaggregate purchasing behavior of a sample of a population relevant tothe user, and may provide statistical/aggregate information on thepurchasing behavior for the user as a guide to facilitate the user'sshopping. For example, with reference to FIG. 48A, the discoveryshopping mode 4801 may provide a view of aggregate consumer behavior,divided based on product category (see 4802). Thus, the virtual walletapplication may provide visualization of the magnitude of consumerexpenditure in particular market segment, and generate visual depictionsrepresentative of those magnitudes of consumer expenditure (see4803-4806). In some embodiments, the virtual wallet application may alsoprovide an indicator (see 4809) of the relative expenditure of the userof the virtual wallet application (see blue bars); thus the user may beable to visualize the differences between the user's purchasing behaviorand consumer behavior in the aggregate. The user may be able to turn offthe user's purchasing behavior indicator (see 4810). In someembodiments, the virtual wallet application may allow the user to zoomin to and out of the visualization, so that the user may obtain a viewwith the appropriate amount of granularity as per the user's desire (see4807-4808). At any time, the user may be able to reset the visualizationto a default perspective (see 4811).

Similarly, the discovery shopping mode 4821 may provide a view ofaggregate consumer response to opinions of experts, divided based onopinions of experts aggregated form across the web (see 4802). Thus, thevirtual wallet application may provide visualizations of how wellconsumers tend to agree with various expert opinion on various productcategories, and whose opinions matter to consumers in the aggregate (see4823-4826). In some embodiments, the virtual wallet application may alsoprovide an indicator (see 4829) of the relative expenditure of the userof the virtual wallet application (see blue bars); thus the user may beable to visualize the differences between the user's purchasing behaviorand consumer behavior in the aggregate. The user may be able to turn offthe user's purchasing behavior indicator (see 4830). In someembodiments, the virtual wallet application may allow the user to zoomin to and out of the visualization, so that the user may obtain a viewwith the appropriate amount of granularity as per the user's desire (see4827-4828). At any time, the user may be able to reset the visualizationto a default perspective (see 4831).

With reference to FIG. 48B, in some implementations, the virtual walletapplication may allow users to create targeted shopping rules forpurchasing (see FIG. 48A, 4812, 4822). For example, the user may utilizethe consumer aggregate behavior and the expert opinion data to craftrules on when to initiate purchases automatically. As an example, rule4841 specifies that the virtual wallet should sell the users iPad2 ifits consumer reports rating falls below 3.75/5.0, before March 1,provided a sale price of $399 can be obtained. As another example, rule4842 specifies that the virtual wallet should buy an iPad3 if rule 4841succeeds before February 15. As another example, rule 4843 specifiesthat the wallet should buy a Moto Droid Razr from the Android Market forless than $349.99 if its Slashdot rating is greater than 3.75 beforeFebruary 1. Similarly, numerous rules with a wide variety of variationsand dependencies may be generated for targeted shopping in the discoverymode. In some implementations, the virtual wallet user may allow theuser to modify a rule. For example, the wallet may provide the user withan interface similar to 4846 or 4847. The user may utilize toolsavailable in the rule editor toolbox to design the rule according to theuser's desires. In some implementations, the wallet may also provide amarket status for the items that are subject to the targeted shoppingrules.

With reference to FIG. 48C, in some implementations, the virtual walletapplication may provide a market watch feature, wherein the trendsassociated with items subject to targeted shopping rules may be trackedand visually represented for the user. For example, the visualizationmay take, in some implementations, the form of a ticker table, whereinagainst each item 4851(A)-(E) are listed a product category or clusterof expert opinions to which the product is related 4852, pricingindicators, including, but not limited to: price at the time of rulecreation 4852, price at the time of viewing the market watch screen4853, and a target price for the items (A)-(E). Based on the prices, themarket watch screen may provide a trending symbol (e.g., up, down, nochange, etc.) for each item that is subject to a targeted shopping rule.Where an item satisfied the targeted rule (see item (E)), the virtualwallet may automatically initiate a purchase transaction for that itemonce the target price is satisfied.

EXDB Controller

FIG. 49 shows a block diagram illustrating embodiments of a EXDBcontroller. In this embodiment, the EXDB controller 4901 may serve toaggregate, process, store, search, serve, identify, instruct, generate,match, and/or facilitate interactions with a computer through varioustechnologies, and/or other related data.

Typically, users, which may be people and/or other systems, may engageinformation technology systems (e.g., computers) to facilitateinformation processing. In turn, computers employ processors to processinformation; such processors 4903 may be referred to as centralprocessing units (CPU). One form of processor is referred to as amicroprocessor. CPUs use communicative circuits to pass binary encodedsignals acting as instructions to enable various operations. Theseinstructions may be operational and/or data instructions containingand/or referencing other instructions and data in various processoraccessible and operable areas of memory 4929 (e.g., registers, cachememory, random access memory, etc.). Such communicative instructions maybe stored and/or transmitted in batches (e.g., batches of instructions)as programs and/or data components to facilitate desired operations.These stored instruction codes, e.g., programs, may engage the CPUcircuit components and other motherboard and/or system components toperform desired operations. One type of program is a computer operatingsystem, which, may be executed by CPU on a computer; the operatingsystem enables and facilitates users to access and operate computerinformation technology and resources. Some resources that may beemployed in information technology systems include: input and outputmechanisms through which data may pass into and out of a computer;memory storage into which data may be saved; and processors by whichinformation may be processed. These information technology systems maybe used to collect data for later retrieval, analysis, and manipulation,which may be facilitated through a database program. These informationtechnology systems provide interfaces that allow users to access andoperate various system components.

In one embodiment, the EXDB controller 4901 may be connected to and/orcommunicate with entities such as, but not limited to: one or more usersfrom user input devices 4911; peripheral devices 4912; an optionalcryptographic processor device 4928; and/or a communications network4913.

Networks are commonly thought to comprise the interconnection andinteroperation of clients, servers, and intermediary nodes in a graphtopology. It should be noted that the term “server” as used throughoutthis application refers generally to a computer, other device, program,or combination thereof that processes and responds to the requests ofremote users across a communications network. Servers serve theirinformation to requesting “clients.” The term “client” as used hereinrefers generally to a computer, program, other device, user and/orcombination thereof that is capable of processing and making requestsand obtaining and processing any responses from servers across acommunications network. A computer, other device, program, orcombination thereof that facilitates, processes information andrequests, and/or furthers the passage of information from a source userto a destination user is commonly referred to as a “node.” Networks aregenerally thought to facilitate the transfer of information from sourcepoints to destinations. A node specifically tasked with furthering thepassage of information from a source to a destination is commonly calleda “router.” There are many forms of networks such as Local Area Networks27 (LANs), Pico networks, Wide Area Networks (WANs), Wireless Networks(WLANs), etc. For example, the Internet is generally accepted as beingan interconnection of a multitude of networks whereby remote clients andservers may access and interoperate with one another.

The EXDB controller 4901 may be based on computer systems that maycomprise, but are not limited to, components such as: a computersystemization 4902 connected to memory 4929.

Computer Systemization

A computer systemization 4902 may comprise a clock 4930, centralprocessing unit (“CPU(s)” and/or “processor(s)” (these terms are usedinterchangeable throughout the disclosure unless noted to the contrary))4903, a memory 4929 (e.g., a read only memory (ROM) 4906, a randomaccess memory (RAM) 4905, etc.), and/or an interface bus 4907, and mostfrequently, although not necessarily, are all interconnected and/orcommunicating through a system bus 4904 on one or more (mother)board(s)4902 having conductive and/or otherwise transportive circuit pathwaysthrough which instructions (e.g., binary encoded signals) may travel toeffectuate communications, operations, storage, etc. The computersystemization may be connected to a power source 4986; e.g., optionallythe power source may be internal. Optionally, a cryptographic processor4926 and/or transceivers (e.g., ICs) 4974 may be connected to the systembus. In another embodiment, the cryptographic processor and/ortransceivers may be connected as either internal and/or externalperipheral devices 4912 via the interface bus I/O. In turn, thetransceivers may be connected to antenna(s) 4975, thereby effectuatingwireless transmission and reception of various communication and/orsensor protocols; for example the antenna(s) may connect to: a TexasInstruments WiLink WL1283 transceiver chip (e.g., providing 802.1 in,Bluetooth 3.0, FM, global positioning system (GPS) (thereby allowingEXDB controller to determine its location)); Broadcom BCM4329FKUBGtransceiver chip (e.g., providing 802.1 in, Bluetooth 2.1+EDR, FM,etc.); a Broadcom BCM47501UB8 receiver chip (e.g., GPS); an InfineonTechnologies X-Gold 618-PMB9800 (e.g., providing 2G/3G HSDPA/HSUPAcommunications); and/or the like. The system clock typically has acrystal oscillator and generates a base signal through the computersystemization's circuit pathways. The clock is typically coupled to thesystem bus and various clock multipliers that will increase or decreasethe base operating frequency for other components interconnected in thecomputer systemization. The clock and various components in a computersystemization drive signals embodying information throughout the system.Such transmission and reception of instructions embodying informationthroughout a computer systemization may be commonly referred to ascommunications. These communicative instructions may further betransmitted, received, and the cause of return and/or replycommunications beyond the instant computer systemization to:communications networks, input devices, other computer systemizations,peripheral devices, and/or the like. It should be understood that inalternative embodiments, any of the above components may be connecteddirectly to one another, connected to the CPU, and/or organized innumerous variations employed as exemplified by various computer systems.

The CPU comprises at least one high-speed data processor adequate toexecute program components for executing user and/or system-generatedrequests. Often, the processors themselves will incorporate variousspecialized processing units, such as, but not limited to: integratedsystem (bus) controllers, memory management control units, floatingpoint units, and even specialized processing sub-units like graphicsprocessing units, digital signal processing units, and/or the like.Additionally, processors may include internal fast access addressablememory, and be capable of mapping and addressing memory 4929 beyond theprocessor itself; internal memory may include, but is not limited to:fast registers, various levels of cache memory (e.g., level 1, 2, 3,etc.), RAM, etc. The processor may access this memory through the use ofa memory address space that is accessible via instruction address, whichthe processor can construct and decode allowing it to access a circuitpath to a specific memory address space having a memory state. The CPUmay be a microprocessor such as: AMD's Athlon, Duron and/or Opteron;ARM's application, embedded and secure processors; IBM and/or Motorola'sDragonBall and PowerPC; IBM's and Sony's Cell processor; Intel'sCeleron, Core (2) Duo, Itanium, Pentium, Xeon, and/or XScale; and/or thelike processor(s). The CPU interacts with memory through instructionpassing through conductive and/or transportive conduits (e.g., (printed)electronic and/or optic circuits) to execute stored instructions (i.e.,program code) according to conventional data processing techniques. Suchinstruction passing facilitates communication within the EXDB controllerand beyond through various interfaces. Should processing requirementsdictate a greater amount speed and/or capacity, distributed processors(e.g., Distributed EXDB), mainframe, multi-core, parallel, and/orsuper-computer architectures may similarly be employed. Alternatively,should deployment requirements dictate greater portability, smallerPersonal Digital Assistants (PDAs) may be employed.

Depending on the particular implementation, features of the EXDB may beachieved by implementing a microcontroller such as CAST's R8051XC2microcontroller; Intel's MCS 51 (i.e., 8051 microcontroller); and/or thelike. Also, to implement certain features of the EXDB, some featureimplementations may rely on embedded components, such as:Application-Specific Integrated Circuit (“ASIC”), Digital SignalProcessing (“DSP”), Field Programmable Gate Array (“FPGA”), and/or thelike embedded technology. For example, any of the EXDB componentcollection (distributed or otherwise) and/or features may be implementedvia the microprocessor and/or via embedded components; e.g., via ASIC,coprocessor, DSP, FPGA, and/or the like. Alternately, someimplementations of the EXDB may be implemented with embedded componentsthat are configured and used to achieve a variety of features or signalprocessing.

Depending on the particular implementation, the embedded components mayinclude software solutions, hardware solutions, and/or some combinationof both hardware/software solutions. For example, EXDB featuresdiscussed herein may be achieved through implementing FPGAs, which are asemiconductor devices containing programmable logic components called“logic blocks”, and programmable interconnects, such as the highperformance FPGA Virtex series and/or the low cost Spartan seriesmanufactured by Xilinx. Logic blocks and interconnects can be programmedby the customer or designer, after the FPGA is manufactured, toimplement any of the EXDB features. A hierarchy of programmableinterconnects allow logic blocks to be interconnected as needed by theEXDB system designer/administrator, somewhat like a one-chipprogrammable breadboard. An FPGA's logic blocks can be programmed toperform the operation of basic logic gates such as AND, and XOR, or morecomplex combinational operators such as decoders or mathematicaloperations. In most FPGAs, the logic blocks also include memoryelements, which may be circuit flip-flops or more complete blocks ofmemory. In some circumstances, the EXDB may be developed on regularFPGAs and then migrated into a fixed version that more resembles ASICimplementations. Alternate or coordinating implementations may migrateEXDB controller features to a final ASIC instead of or in addition toFPGAs. Depending on the implementation all of the aforementionedembedded components and microprocessors may be considered the “CPU”and/or 7 “processor” for the EXDB.

Power Source

The power source 4986 may be of any standard form for powering smallelectronic circuit board devices such as the following power cells:alkaline, lithium hydride, lithium ion, lithium polymer, nickel cadmium,solar cells, and/or the like. Other types of AC or DC power sources maybe used as well. In the case of solar cells, in one embodiment, the caseprovides an aperture through which the solar cell may capture photonicenergy. The power cell 4986 is connected to at least one of theinterconnected subsequent components of the EXDB thereby providing anelectric current to all subsequent components. In one example, the powersource 4986 is connected to the system bus component 4904. In analternative embodiment, an outside power source 4986 is provided througha connection across the I/O 4908 interface. For example, a USB and/orIEEE 1394 connection carries both data and power across the connectionand is therefore a suitable source of power.

Interface Adapters

Interface bus(ses) 4907 may accept, connect, and/or communicate to anumber of interface adapters, conventionally although not necessarily inthe form of adapter cards, such as but not limited to: input outputinterfaces (I/O) 4908, storage interfaces 4909, network interfaces 4910,and/or the like. Optionally, cryptographic processor interfaces 4927similarly may be connected to the interface bus. The interface busprovides for the communications of interface adapters with one anotheras well as with other components of the computer systemization.Interface adapters are adapted for a compatible interface bus. Interfaceadapters conventionally connect to the interface bus via a slotarchitecture. Conventional slot architectures may be employed, such as,but not limited to: Accelerated Graphics Port (AGP), Card Bus,(Extended) Industry Standard Architecture ((E)ISA), Micro ChannelArchitecture (MCA), NuBus, Peripheral Component Interconnect (Extended)(PCI(X)), PCI Express, Personal Computer Memory Card InternationalAssociation (PCMCIA), and/or the like.

Storage interfaces 4909 may accept, communicate, and/or connect to anumber of storage devices such as, but not limited to: storage devices4914, removable disc devices, and/or the like. Storage interfaces mayemploy connection protocols such as, but not limited to: (Ultra)(Serial) Advanced Technology Attachment (Packet Interface) ((Ultra)(Serial) ATA(PI)), (Enhanced) Integrated Drive Electronics ((E)IDE),Institute of Electrical and Electronics Engineers (IEEE) 1394, fiberchannel, Small Computer Systems Interface (SCSI), Universal Serial Bus(USB), and/or the like.

Network interfaces 4910 may accept, communicate, and/or connect to acommunications network 4913. Through a communications network 4913, theEXDB controller is accessible through remote clients 4933 b (e.g.,computers with web browsers) by users 4933 a. Network interfaces mayemploy connection protocols such as, but not limited to: direct connect,Ethernet (thick, thin, twisted pair 10/100/1000 Base T, and/or thelike), Token Ring, wireless connection such as IEEE 802.11a-x, and/orthe like. Should processing requirements dictate a greater amount speedand/or capacity, distributed network controllers (e.g., DistributedEXDB), architectures may similarly be employed to pool, load balance,and/or otherwise increase the communicative bandwidth required by theEXDB controller. A communications network may be any one and/or thecombination of the following: a direct interconnection; the Internet; aLocal Area Network (LAN); a Metropolitan Area Network (MAN); anOperating Missions as Nodes on the Internet (OMNI); a secured customconnection; a Wide Area Network (WAN); a wireless network (e.g.,employing protocols such as, but not limited to a Wireless ApplicationProtocol (WAP), I-mode, and/or the like); and/or the like. A networkinterface may be regarded as a specialized form of an input outputinterface. Further, multiple network interfaces 4910 may be used toengage with various communications network types 4913. For example,multiple network interfaces may be employed to allow for thecommunication over broadcast, multicast, and/or unicast networks.

Input Output interfaces (I/O) 4908 may accept, communicate, and/orconnect to user input devices 4911, peripheral devices 4912,cryptographic processor devices 4928, and/or the like. I/O may employconnection protocols such as, but not limited to: audio: analog,digital, monaural, RCA, stereo, and/or the like; data: Apple Desktop Bus(ADB), IEEE 1394a-b, serial, universal serial bus (USB); infrared;joystick; keyboard; midi; optical; PC AT; PS/2; parallel; radio; videointerface: Apple Desktop Connector (ADC), BNC, coaxial, component,composite, digital, Digital Visual Interface (DVI), high-definitionmultimedia interface (HDMI), RCA, RF antennae, S-Video, VGA, and/or thelike; wireless transceivers: 802.11a/b/g/n/x; Bluetooth; cellular (e.g.,code division multiple access (CDMA), high speed packet access(HSPA(+)), high-speed downlink packet access (HSDPA), global system formobile communications (GSM), long term evolution (LTE), WiMax, etc.);and/or the like. One typical output device may include a video display,which typically comprises a Cathode Ray Tube (CRT) or Liquid CrystalDisplay (LCD) based monitor with an interface (e.g., DVI circuitry andcable) that accepts signals from a video interface, may be used. Thevideo interface composites information generated by a computersystemization and generates video signals based on the compositedinformation in a video memory frame. Another output device is atelevision set, which accepts signals from a video interface. Typically,the video interface provides the composited video information through avideo connection interface that accepts a video display interface (e.g.,an RCA composite video connector accepting an RCA composite video cable;a DVI connector accepting a DVI display cable, etc.).

User input devices 4911 often are a type of peripheral device 512 (seebelow) and may include: card readers, dongles, finger print readers,gloves, graphics tablets, joysticks, keyboards, microphones, mouse(mice), remote controls, retina readers, touch screens (e.g.,capacitive, resistive, etc.), trackballs, trackpads, sensors (e.g.,accelerometers, ambient light, GPS, gyroscopes, proximity, etc.),styluses, and/or the like.

Peripheral devices 4912 may be connected and/or communicate to I/Oand/or other facilities of the like such as network interfaces, storageinterfaces, directly to the interface bus, system bus, the CPU, and/orthe like. Peripheral devices may be external, internal and/or part ofthe EXDB controller. Peripheral devices may include: antenna, audiodevices (e.g., line-in, line-out, microphone input, speakers, etc.),cameras (e.g., still, video, webcam, etc.), dongles (e.g., for copyprotection, ensuring secure transactions with a digital signature,and/or the like), external processors (for added capabilities; e.g.,crypto devices 528), force-feedback devices (e.g., vibrating motors),network interfaces, printers, scanners, storage devices, transceivers(e.g., cellular, GPS, etc.), video devices (e.g., goggles, monitors,etc.), video sources, visors, and/or the like. Peripheral devices ofteninclude types of input devices (e.g., cameras).

It should be noted that although user input devices and peripheraldevices may be employed, the EXDB controller may be embodied as anembedded, dedicated, and/or monitor-less (i.e., headless) device,wherein access would be provided over a network interface connection.

Cryptographic units such as, but not limited to, microcontrollers,processors 4926, interfaces 4927, and/or devices 4928 may be attached,and/or communicate with the EXDB controller. A MC68HC16 microcontroller,manufactured by Motorola Inc., may be used for and/or withincryptographic units. The MC68HC16 microcontroller utilizes a 16-bitmultiply-and-accumulate instruction in the 16 MHz configuration andrequires less than one second to perform a 512-bit RSA private keyoperation. Cryptographic units support the authentication ofcommunications from interacting agents, as well as allowing foranonymous transactions. Cryptographic units may also be configured aspart of the CPU. Equivalent microcontrollers and/or processors may alsobe used. Other commercially available specialized cryptographicprocessors include: Broadcom's CryptoNetX and other Security Processors;nCipher's nShield; SafeNet's Luna PCI (e.g., 7100) series; SemaphoreCommunications' 40 MHz Roadrunner 184; Sun's Cryptographic Accelerators(e.g., Accelerator 6000 PCIe Board, Accelerator 500 Daughtercard); ViaNano Processor (e.g., L2100, L2200, U2400) line, which is capable ofperforming 500+ MB/s of cryptographic instructions; VLSI Technology's 33MHz 6868; and/or the like.

Memory

Generally, any mechanization and/or embodiment allowing a processor toaffect the storage and/or retrieval of information is regarded as memory4929. However, memory is a fungible technology and resource, thus, anynumber of memory embodiments may be employed in lieu of or in concertwith one another. It is to be understood that the EXDB controller and/ora computer systemization may employ various forms of memory 4929. Forexample, a computer systemization may be configured wherein theoperation of on-chip CPU memory (e.g., registers), RAM, ROM, and anyother storage devices are provided by a paper punch tape or paper punchcard mechanism; however, such an embodiment would result in an extremelyslow rate of operation. In a typical configuration, memory 4929 willinclude ROM 4906, RAM 4905, and a storage device 4914. A storage device4914 may be any conventional computer system storage. Storage devicesmay include a drum; a (fixed and/or removable) magnetic disk drive; amagneto-optical drive; an optical drive (i.e., Blueray, CDROM/RAM/Recordable (R)/ReWritable (RW), DVD R/RW, HD DVD R/RW etc.); anarray of devices (e.g., Redundant Array of Independent Disks (RAID));solid state memory devices (USB memory, solid state drives (SSD), etc.);other processor-readable storage mediums; and/or other devices of thelike. Thus, a computer systemization generally requires and makes use ofmemory.

Component Collection

The memory 4929 may contain a collection of program and/or databasecomponents and/or data such as, but not limited to: operating systemcomponent(s) 4915 (operating system); information server component(s)4916 (information server); user interface component(s) 4917 (userinterface); Web browser component(s) 4918 (Web browser); database(s)4919; mail server component(s) 4921; mail client component(s) 4922;cryptographic server component(s) 4920 (cryptographic server); the EXDBcomponent(s) 4935; and/or the like (i.e., collectively a componentcollection). These components may be stored and accessed from thestorage devices and/or from storage devices accessible through aninterface bus. Although non-conventional program components such asthose in the component collection, typically, are stored in a localstorage device 4914, they may also be loaded and/or stored in memorysuch as: peripheral devices, RAM, remote storage facilities through acommunications network, ROM, various forms of memory, and/or the like.

Operating System

The operating system component 4915 is an executable program componentfacilitating the operation of the EXDB controller. Typically, theoperating system facilitates access of I/O, network interfaces,peripheral devices, storage devices, and/or the like. The operatingsystem may be a highly fault tolerant, scalable, and secure system suchas: Apple Macintosh OS X (Server); AT&T Nan 9; Be OS; Unix and Unix-likesystem distributions (such as AT&T's UNIX; Berkley Software Distribution(BSD) variations such as FreeBSD, NetBSD, OpenBSD, and/or the like;Linux distributions such as Red Hat, Ubuntu, and/or the like); and/orthe like operating systems. However, more limited and/or less secureoperating systems also may be employed such as Apple Macintosh OS, IBMOS/2, Microsoft DOS, Microsoft Windows2000/2003/3.1/95/98/CE/Millenium/NT/Vista/XP/Win7 (Server), Palm OS,and/or the like. An operating system may communicate to and/or withother components in a component collection, including itself, and/or thelike. Most frequently, the operating system communicates with otherprogram components, user interfaces, and/or the like. For example, theoperating system may contain, communicate, generate, obtain, and/orprovide program component, system, user, and/or data communications,requests, and/or responses. The operating system, once executed by theCPU, may enable the interaction with communications networks, data, I/O,peripheral devices, program components, memory, user input devices,and/or the like. The operating system may provide communicationsprotocols that allow the EXDB controller to communicate with otherentities through a communications network 4913. Various communicationprotocols may be used by the EXDB controller as a subcarrier transportmechanism for interaction, such as, but not limited to: multicast,TCP/IP, UDP, unicast, and/or the like.

Information Server

An information server component 4916 is a stored program component thatis executed by a CPU. The information server may be a conventionalInternet information server such as, but not limited to Apache SoftwareFoundation's Apache, Microsoft's Internet Information Server, and/or thelike. The information server may allow for the execution of programcomponents through facilities such as Active Server Page (ASP), ActiveX,(ANSI) (Objective−) C (++), C# and/or .NET, Common Gateway Interface(CGI) scripts, dynamic (D) hypertext markup language (HTML), FLASH,Java, JavaScript, Practical Extraction Report Language (PERL), HypertextPre-Processor (PHP), pipes, Python, wireless application protocol (WAP),WebObjects, and/or the like. The information server may support securecommunications protocols such as, but not limited to, File TransferProtocol (FTP); HyperText Transfer Protocol (HTTP); Secure HypertextTransfer Protocol (HTTPS), Secure Socket Layer (SSL), messagingprotocols (e.g., America Online (AOL) Instant Messenger (AIM),Application Exchange (APEX), ICQ, Internet Relay Chat (IRC), MicrosoftNetwork (MSN) Messenger Service, Presence and Instant Messaging Protocol(PRIM), Internet Engineering Task Force's (IETF's) Session InitiationProtocol (SIP), SIP for Instant Messaging and Presence LeveragingExtensions (SIMPLE), open XML-based Extensible Messaging and PresenceProtocol (XMPP) (i.e., Jabber or Open Mobile Alliance's (OMA's) InstantMessaging and Presence Service (IMPS)), Yahoo! Instant MessengerService, and/or the like. The information server provides results in theform of Web pages to Web browsers, and allows for the manipulatedgeneration of the Web pages through interaction with other programcomponents. After a Domain Name System (DNS) resolution portion of anHTTP request is resolved to a particular information server, theinformation server resolves requests for information at specifiedlocations on the EXDB controller based on the remainder of the HTTPrequest. For example, a request such ashttp://123.124.125.126/myInformation.html might have the IP portion ofthe request “123.124.125.126” resolved by a DNS server to an informationserver at that IP address; that information server might in turn furtherparse the http request for the “/myInformation.html” portion of therequest and resolve it to a location in memory containing theinformation “myInformation.html.” Additionally, other informationserving protocols may be employed across various ports, e.g., FTPcommunications across port 21, and/or the like. An information servermay communicate to and/or with other components in a componentcollection, including itself, and/or facilities of the like. Mostfrequently, the information server communicates with the EXDB database4919, operating systems, other program components, user interfaces, Webbrowsers, and/or the like.

Access to the EXDB database may be achieved through a number of databasebridge mechanisms such as through scripting languages as enumeratedbelow (e.g., CGI) and through inter-application communication channelsas enumerated below (e.g., CORBA, WebObjects, etc.). Any data requeststhrough a Web browser are parsed through the bridge mechanism intoappropriate grammars as required by the EXDB. In one embodiment, theinformation server would provide a Web form accessible by a Web browser.Entries made into supplied fields in the Web form are tagged as havingbeen entered into the particular fields, and parsed as such. The enteredterms are then passed along with the field tags, which act to instructthe parser to generate queries directed to appropriate tables and/orfields. In one embodiment, the parser may generate queries in standardSQL by instantiating a search string with the proper join/selectcommands based on the tagged text entries, wherein the resulting commandis provided over the bridge mechanism to the EXDB as a query. Upongenerating query results from the query, the results are passed over thebridge mechanism, and may be parsed for formatting and generation of anew results Web page by the bridge mechanism. Such a new results Webpage is then provided to the information server, which may supply it tothe requesting Web browser.

Also, an information server may contain, communicate, generate, obtain,and/or provide program component, system, user, and/or datacommunications, requests, and/or responses.

User Interface

Computer interfaces in some respects are similar to automobile operationinterfaces. Automobile operation interface elements such as steeringwheels, gearshifts, and speedometers facilitate the access, operation,and display of automobile resources, and status. Computer interactioninterface elements such as check boxes, cursors, menus, scrollers, andwindows (collectively and commonly referred to as widgets) similarlyfacilitate the access, capabilities, operation, and display of data andcomputer hardware and operating system resources, and status. Operationinterfaces are commonly called user interfaces. Graphical userinterfaces (GUIs) such as the Apple Macintosh Operating System's Aqua,IBM's OS/2, Microsoft's Windows2000/2003/3.1/95/98/CE/Millenium/NT/XP/Vista/7 (i.e., Aero), Unix'sX-Windows (e.g., which may include additional Unix graphic interfacelibraries and layers such as K Desktop Environment (KDE), mythTV and GNUNetwork Object Model Environment (GNOME)), web interface libraries(e.g., ActiveX, AJAX, (D)HTML, FLASH, Java, JavaScript, etc. interfacelibraries such as, but not limited to, Dojo, jQuery UI, MooTools,Prototype, script.aculo.us, SWFObject, Yahoo! User Interface, any ofwhich may be used and provide a baseline and means of accessing anddisplaying information graphically to users.

A user interface component 4917 is a stored program component that isexecuted by a CPU. The user interface may be a conventional graphic userinterface as provided by, with, and/or atop operating systems and/oroperating environments such as already discussed. The user interface mayallow for the display, execution, interaction, manipulation, and/oroperation of program components and/or system facilities through textualand/or graphical facilities. The user interface provides a facilitythrough which users may affect, interact, and/or operate a computersystem. A user interface may communicate to and/or with other componentsin a component collection, including itself, and/or facilities of thelike. Most frequently, the user interface communicates with operatingsystems, other program components, and/or the like. The user interfacemay contain, communicate, generate, obtain, and/or provide programcomponent, system, user, and/or data communications, requests, and/orresponses.

Web Browser

A Web browser component 4918 is a stored program component that isexecuted by a CPU. The Web browser may be a conventional hypertextviewing application such as Microsoft Internet Explorer or NetscapeNavigator. Secure Web browsing may be supplied with 128 bit (or greater)encryption by way of HTTPS, SSL, and/or the like. Web browsers allowingfor the execution of program components through facilities such asActiveX, AJAX, (D)HTML, FLASH, Java, JavaScript, web browser plug-inAPIs (e.g., Firefox, Safari Plug-in, and/or the like APIs), and/or thelike. Web browsers and like information access tools may be integratedinto PDAs, cellular telephones, and/or other mobile devices. A Webbrowser may communicate to and/or with other components in a componentcollection, including itself, and/or facilities of the like. Mostfrequently, the Web browser communicates with information servers,operating systems, integrated program components (e.g., plug-ins),and/or the like; e.g., it may contain, communicate, generate, obtain,and/or provide program component, system, user, and/or datacommunications, requests, and/or responses. Also, in place of a Webbrowser and information server, a combined application may be developedto perform similar operations of both. The combined application wouldsimilarly affect the obtaining and the provision of information tousers, user agents, and/or the like from the EXDB enabled nodes. Thecombined application may be nugatory on systems employing standard Webbrowsers.

Mail Server

A mail server component 4921 is a stored program component that isexecuted by a CPU 4903. The mail server may be a conventional Internetmail server such as, but not limited to sendmail, Microsoft Exchange,and/or the like. The mail server may allow for the execution of programcomponents through facilities such as ASP, ActiveX, (ANSI) (Objective−)C (++), C# and/or .NET, CGI scripts, Java, JavaScript, PERL, PHP, pipes,Python, WebObjects, and/or the like. The mail server may supportcommunications protocols such as, but not limited to: Internet messageaccess protocol (IMAP), Messaging Application Programming Interface(MAPI)/Microsoft Exchange, post office protocol (POP3), simple mailtransfer protocol (SMTP), and/or the like. The mail server can route,forward, and process incoming and outgoing mail messages that have beensent, relayed and/or otherwise traversing through and/or to the EXDB.

Access to the EXDB mail may be achieved through a number of APIs offeredby the individual Web server components and/or the operating system.

Also, a mail server may contain, communicate, generate, obtain, and/orprovide program component, system, user, and/or data communications,requests, information, and/or responses.

Mail Client

A mail client component 4922 is a stored program component that isexecuted by a CPU 4903. The mail client may be a conventional mailviewing application such as Apple Mail, Microsoft Entourage, MicrosoftOutlook, Microsoft Outlook Express, Mozilla, Thunderbird, and/or thelike. Mail clients may support a number of transfer protocols, such as:IMAP, Microsoft Exchange, POP3, SMTP, and/or the like. A mail client maycommunicate to and/or with other components in a component collection,including itself, and/or facilities of the like. Most frequently, themail client communicates with mail servers, operating systems, othermail clients, and/or the like; e.g., it may contain, communicate,generate, obtain, and/or provide program component, system, user, and/ordata communications, requests, information, and/or responses. Generally,the mail client provides a facility to compose and transmit electronicmail messages.

Cryptographic Server

A cryptographic server component 4920 is a stored program component thatis executed by a CPU 4903, cryptographic processor 4926, cryptographicprocessor interface 4927, cryptographic processor device 4928, and/orthe like. Cryptographic processor interfaces will allow for expeditionof encryption and/or decryption requests by the cryptographic component;however, the cryptographic component, alternatively, may run on aconventional CPU. The cryptographic component allows for the encryptionand/or decryption of provided data. The cryptographic component allowsfor both symmetric and asymmetric (e.g., Pretty Good Protection (PGP))encryption and/or decryption. The cryptographic component may employcryptographic techniques such as, but not limited to: digitalcertificates (e.g., X.509 authentication framework), digital signatures,dual signatures, enveloping, password access protection, public keymanagement, and/or the like. The cryptographic component will facilitatenumerous (encryption and/or decryption) security protocols such as, butnot limited to: checksum, Data Encryption Standard (DES), EllipticalCurve Encryption (ECC), International Data Encryption Algorithm (IDEA),Message Digest 5 (MD5, which is a one way hash operation), passwords,Rivest Cipher (RC5), Rijndael, RSA (which is an Internet encryption andauthentication system that uses an algorithm developed in 1977 by RonRivest, Adi Shamir, and Leonard Adleman), Secure Hash Algorithm (SHA),Secure Socket Layer (SSL), Secure Hypertext Transfer Protocol (HTTPS),and/or the like. Employing such encryption security protocols, the EXDBmay encrypt all incoming and/or outgoing communications and may serve asnode within a virtual private network (VPN) with a wider communicationsnetwork. The cryptographic component facilitates the process of“security authorization” whereby access to a resource is inhibited by asecurity protocol wherein the cryptographic component effects authorizedaccess to the secured resource. In addition, the cryptographic componentmay provide unique identifiers of content, e.g., employing and MD5 hashto obtain a unique signature for an digital audio file. A cryptographiccomponent may communicate to and/or with other components in a componentcollection, including itself, and/or facilities of the like. Thecryptographic component supports encryption schemes allowing for thesecure transmission of information across a communications network toenable the EXDB component to engage in secure transactions if sodesired. The cryptographic component facilitates the secure accessing ofresources on the EXDB and facilitates the access of secured resources onremote systems; i.e., it may act as a client and/or server of securedresources. Most frequently, the cryptographic component communicateswith information servers, operating systems, other program components,and/or the like. The cryptographic component may contain, communicate,generate, obtain, and/or provide program component, system, user, and/ordata communications, requests, and/or responses.

The EXDB Database

The EXDB database component 4919 may be embodied in a database and itsstored data. The database is a stored program component, which isexecuted by the CPU; the stored program component portion configuringthe CPU to process the stored data. The database may be a conventional,fault tolerant, relational, scalable, secure database such as Oracle orSybase. Relational databases are an extension of a flat file. Relationaldatabases consist of a series of related tables. The tables areinterconnected via a key field. Use of the key field allows thecombination of the tables by indexing against the key field; i.e., thekey fields act as dimensional pivot points for combining informationfrom various tables. Relationships generally identify links maintainedbetween tables by matching primary keys. Primary keys represent fieldsthat uniquely identify the rows of a table in a relational database.More precisely, they uniquely identify rows of a table on the “one” sideof a one-to-many relationship.

Alternatively, the EXDB database may be implemented using variousstandard data-structures, such as an array, hash, (linked) list, struct,structured text file (e.g., XML), table, and/or the like. Suchdata-structures may be stored in memory and/or in (structured) files. Inanother alternative, an object-oriented database may be used, such asFrontier, ObjectStore, Poet, Zope, and/or the like. Object databases caninclude a number of object collections that are grouped and/or linkedtogether by common attributes; they may be related to other objectcollections by some common attributes. Object-oriented databases performsimilarly to relational databases with the exception that objects arenot just pieces of data but may have other types of capabilitiesencapsulated within a given object. If the EXDB database is implementedas a data-structure, the use of the EXDB database 4919 may be integratedinto another component such as the EXDB component 4935. Also, thedatabase may be implemented as a mix of data structures, objects, andrelational structures. Databases may be consolidated and/or distributedin countless variations through standard data processing techniques.Portions of databases, e.g., tables, may be exported and/or imported andthus decentralized and/or integrated.

In one embodiment, the database component 4919 includes several tables4919 a-w. A Users table 4919 a may include fields such as, but notlimited to: user_id, ssn, dob, first_name, last_name, age, state,address_firstline, address_secondline, zipcode, devices_list,contact_info, contact_type, alt contact_info, alt contact_type, and/orthe like. The Users table may support and/or track multiple entityaccounts on a EXDB. A Devices table 4919 b may include fields such as,but not limited to: device_id, user_id, client_ip, client_type,client_model, operating_system, os_version, app_installed_flag, and/orthe like. An Apps table 4919 c may include fields such as, but notlimited to: app_id, app_name, app_type, OS_compatibilities_list,version, timestamp, developer_id, and/or the like. An Accounts table4919 d may include fields such as, but not limited to: account_id,account_firstname, account_lastname, account_type, account_num,account_balance_list, billingaddress_line1, billingaddress_line2,billing_zipcode, billing_state, shipping_preferences,shippingaddress_line1, shippingaddress_line2, shipping_zipcode,shipping_state, and/or the like. A Merchants table 4919 e may includefields such as, but not limited to: merchant_id, merchant_name, provimerchant_address, ip_address, mac_address, auth_key, port_num,security_settings_list, and/or the like. An Issuers table 4919 f mayinclude fields such as, but not limited to: issuer_id, issuer_name,issuer_address, ip_address, mac_address, auth_key, port_num,security_settings_list, and/or the like. An Acquirers table 4919 g mayinclude fields such as, but not limited to: acquirer_id,account_firstname, account_lastname, account_type, account_num,account_balance_list, billingaddress_line1, billingaddress_line2,billing_zipcode, billing_state, shipping_preferences,shippingaddress_line1, shippingaddress_line2, shipping_zipcode,shipping_state, and/or the like. A Gateways table 4919 h may includefields such as, but not limited to: gateway_id, gateway_name,merchant_id, issuer_id, acquirer_id, user_id, and/or the like. ATransactions table 4919 i may include fields such as, but not limitedto: transaction_id, order_id, user_id, timestamp, transaction_cost,purchase_details_list, num_products, products_list, product_type,product_params_list, product_title, product_summary, quantity, user_id,client_id, client_ip, client_type, client_model, operating_system,os_version, app_installed_flag, user_id, account_firstname,account_lastname, account_type, account_num, billingaddress_line1,billingaddress_line2, billing_zipcode, billing_state,shipping_preferences, shippingaddress_line1, shippingaddress_line2,shipping_zipcode, shipping_state, merchant_id, merchant_name,merchant_auth_key, and/or the like. A Batches table 4919 j may includefields such as, but not limited to: batch_id, parent_batch_id,transaction_id, account_id, user_id, app_id, batch_rules, and/or thelike. A Ledgers table 4919 k may include fields such as, but not limitedto: ledger_id, transaction_id, user_id, merchant_id, issuer_id,acquirer_id, aggregation_id, ledger_name, ledger_value, and/or the like.A Products table 4919 l may include fields such as, but not limited to:product_id, product_name, sku, price, inventory_level, stores_carrying,unit_of_measure, and/or the like. A Offers table 4919 m may includefields such as, but not limited to: offer_id, merchant_id,offered_to_user_id, offer_type, offer_description, start_date, end_date,num_times_redeemed, and/or the like. A Behavior table 4919 n may includefields such as, but not limited to: behavior_id, user_id,behavior_description, behavior_type, behavior_value, date_time_behavior,and/or the like. An Analytics table 49190 may include fields such as,but not limited to: analytics_id, batch_id, user_id, transaction_id,generated_graph, generated_results_set, generated_results_set_json,input data set, date_time_generated, and/or the like. A Market Datatable 4919 p may include fields such as, but not limited to:market_data_id, index_name, index_value, last_updated_index_datetime,and/or the like. An Input Languages table 4919 q may include fields suchas, but not limited to: input_language_id, function_name,function_definition, parent_input_language_id, mesh_language_id,user_id, tumbler_id, aggregation_id, and/or the like. A Mesh Languagetable 4919 r may include fields such as, but not limited to:mesh_language_id, operation_name, operation_min_test_case,operation_max_test_case, operation_custom_test_case,mesh_language_version, mesh_language_updated_date, and/or the like. ATumblars table 4919 s may include fields such as, but not limited to:tumbler_id, user_visible_model_commands,non_user_visible_model_commands, input_key, output_key, and/or the like.An Aggregation table 4919 t may include fields such as, but not limitedto: aggregation_id, aggregation_data_source, key, value,parent_aggregation_id, and/or the like. A Category table 4919 u mayinclude fields such as, but not limited to: category_id, mesh_id,user_id, category_name, category_type, entity_name, is_present_in_mesh,and/or the like. A Mesh table 4919 v may include fields such as, but notlimited to: mesh_id, mesh_node, mesh_node_value, mesh_edge,mesh_edge_value, mesh_link, mesh_link_value, attributes, tags, keywords,and/or the like. A Price Trends table 4919 w may include fields such as,but not limited to: price_trends_id, merchant_id, date_price_observed,number_observations, observed_price, next_check_date,inventory_quantity, and/or the like.

In one embodiment, the EXDB database may interact with other databasesystems. For example, employing a distributed database system, queriesand data access by search EXDB component may treat the combination ofthe EXDB database, an integrated data security layer database as asingle database entity.

In one embodiment, user programs may contain various user interfaceprimitives, which may serve to update the EXDB. Also, various accountsmay require custom database tables depending upon the environments andthe types of clients the EXDB may need to serve. It should be noted thatany unique fields may be designated as a key field throughout. In analternative embodiment, these tables have been decentralized into theirown databases and their respective database controllers (i.e.,individual database controllers for each of the above tables). Employingstandard data processing techniques, one may further distribute thedatabases over several computer systemizations and/or storage devices.Similarly, configurations of the decentralized database controllers maybe varied by consolidating and/or distributing the various databasecomponents 4919 a-w. The EXDB may be configured to keep track of varioussettings, inputs, and parameters via database controllers.

The EXDB database may communicate to and/or with other components in acomponent collection, including itself, and/or facilities of the like.Most frequently, the EXDB database communicates with the EXDB component,other program components, and/or the like. The database may contain,retain, and provide information regarding other nodes and data.

The EXDBs

The EXDB component 4935 is a stored program component that is executedby a CPU. In one embodiment, the EXDB component incorporates any and/orall combinations of the aspects of the EXDB that was discussed in theprevious figures. As such, the EXDB affects accessing, obtaining and theprovision of information, services, transactions, and/or the like acrossvarious communications networks. The features and embodiments of theEXDB discussed herein increase network efficiency by reducing datatransfer requirements the use of more efficient data structures andmechanisms for their transfer and storage. As a consequence, more datamay be transferred in less time, and latencies with regard totransactions, are also reduced. In many cases, such reduction instorage, transfer time, bandwidth requirements, latencies, etc., willreduce the capacity and structural infrastructure requirements tosupport the EXDB's features and facilities, and in many cases reduce thecosts, energy consumption/requirements, and extend the life of EXDB'sunderlying infrastructure; this has the added benefit of making the EXDBmore reliable. The generation of the mesh graph and dictionary entriesby the EXDB has the technical effect of allowing more transaction,search, enrollment and email data to be analyzed and queried by the EXDBuser without a corresponding increase in data storage server/processinginfrastructure. For example, by utilizing the aggregated data recordnormalization 306, data field recognition 307, entity typeclassification 308, cross-entity correlation 309, and entity attribute310 components of the EXDB, raw aggregated data may be stored in a moreefficient manner and indexed and searched in a manner requiring lessphysical infrastructure and supporting faster querying with reducedlatency (e.g., through the use of a distributed linking node mesh searchcomponent). Aspects of the EXDB facilitate faster transaction processingby reducing consumer decision latency (e.g., through the provision ofcustomized offers requiring decreased user input and therefore reduceddata transfer requirements). Similarly, many of the features andmechanisms are designed to be easier for users to use and access,thereby broadening the audience that may enjoy/employ and exploit thefeature sets of the EXDB; such ease of use also helps to increase thereliability of the EXDB. In addition, the feature sets includeheightened security as noted via the Cryptographic components 4920,4926, 4928 and throughout, making access to the features and data morereliable and secure.

The EXDB component may transform data aggregated from various computerresources via EXDB components into updated entity profiles and/or socialgraphs, and/or the like and use of the EXDB. In one embodiment, the EXDBcomponent 4935 takes inputs such as aggregated data from variouscomputer resources, and transforms the inputs via various components(e.g., SRA 4941, CTE 4942, TDA 4943 SDA 4944, VASE 4945, DFR 4946, ETC4947, CEC 4948, EAA 4949, EPGU 4950, STG 4951, MA 4952, UBPA 4953, UPI4954, TDN 4955, CTC 4956, TDF 4957, CDA 4958, ESA 4959, BAR 4960, AMS4961, ADRN 4962, EXC 4963, CRA 4964, and/or the like), into outputs suchas updated entity profiles and social graphs within the EXDB.

The EXDB component enabling access of information between nodes may bedeveloped by employing standard development tools and languages such as,but not limited to: Apache components, Assembly, ActiveX, binaryexecutables, (ANSI) (Objective−) C (++), C# and/or .NET, databaseadapters, CGI scripts, Java, JavaScript, mapping tools, procedural andobject oriented development tools, PERL, PHP, Python, shell scripts, SQLcommands, web application server extensions, web developmentenvironments and libraries (e.g., Microsoft's ActiveX; Adobe AIR, FLEX &FLASH; AJAX; (D)HTML; Dojo, Java; JavaScript; jQuery(UI); MooTools;Prototype; script.aculo.us; Simple Object Access Protocol (SOAP);SWFObject; Yahoo! User Interface; and/or the like), WebObjects, and/orthe like. In one embodiment, the EXDB server employs a cryptographicserver to encrypt and decrypt communications. The EXDB component maycommunicate to and/or with other components in a component collection,including itself, and/or facilities of the like. Most frequently, theEXDB component communicates with the EXDB database, operating systems,other program components, and/or the like. The EXDB may contain,communicate, generate, obtain, and/or provide program component, system,user, and/or data communications, requests, and/or responses.

Distributed EXDBs

The structure and/or operation of any of the EXDB node controllercomponents may be combined, consolidated, and/or distributed in anynumber of ways to facilitate development and/or deployment. Similarly,the component collection may be combined in any number of ways tofacilitate deployment and/or development. To accomplish this, one mayintegrate the components into a common code base or in a facility thatcan dynamically load the components on demand in an integrated fashion.

The component collection may be consolidated and/or distributed incountless variations through standard data processing and/or developmenttechniques. Multiple instances of any one of the program components inthe program component collection may be instantiated on a single node,and/or across numerous nodes to improve performance throughload-balancing and/or data-processing techniques. Furthermore, singleinstances may also be distributed across multiple controllers and/orstorage devices; e.g., databases. All program component instances andcontrollers working in concert may do so through standard dataprocessing communication techniques.

The configuration of the EXDB controller will depend on the context ofsystem deployment. Factors such as, but not limited to, the budget,capacity, location, and/or use of the underlying hardware resources mayaffect deployment requirements and configuration. Regardless of if theconfiguration results in more consolidated and/or integrated programcomponents, results in a more distributed series of program components,and/or results in some combination between a consolidated anddistributed configuration, data may be communicated, obtained, and/orprovided. Instances of components consolidated into a common code basefrom the program component collection may communicate, obtain, and/orprovide data. This may be accomplished through intra-application dataprocessing communication techniques such as, but not limited to: datareferencing (e.g., pointers), internal messaging, object instancevariable communication, shared memory space, variable passing, and/orthe like.

If component collection components are discrete, separate, and/orexternal to one another, then communicating, obtaining, and/or providingdata with and/or to other component components may be accomplishedthrough inter-application data processing communication techniques suchas, but not limited to: Application Program Interfaces (API) informationpassage; (distributed) Component Object Model ((D)COM), (Distributed)Object Linking and Embedding ((D)OLE), and/or the like), Common ObjectRequest Broker Architecture (CORBA), Jini local and remote applicationprogram interfaces, JavaScript Object Notation (JSON), Remote MethodInvocation (RMI), SOAP, process pipes, shared files, and/or the like.Messages sent between discrete component components forinter-application communication or within memory spaces of a singularcomponent for intra-application communication may be facilitated throughthe creation and parsing of a grammar. A grammar may be developed byusing development tools such as lex, yacc, XML, and/or the like, whichallow for grammar generation and parsing capabilities, which in turn mayform the basis of communication messages within and between components.

For example, a grammar may be arranged to recognize the tokens of anHTTP post command, e.g.:

w3c -post http://... Value1

where Value1 is discerned as being a parameter because “http://” is partof the grammar syntax, and what follows is considered part of the postvalue. Similarly, with such a grammar, a variable “Value1” may beinserted into an “http://” post command and then sent. The grammarsyntax itself may be presented as structured data that is interpretedand/or otherwise used to generate the parsing mechanism (e.g., a syntaxdescription text file as processed by lex, yacc, etc.). Also, once theparsing mechanism is generated and/or instantiated, it itself mayprocess and/or parse structured data such as, but not limited to:character (e.g., tab) delineated text, HTML, structured text streams,XML, and/or the like structured data. In another embodiment,inter-application data processing protocols themselves may haveintegrated and/or readily available parsers (e.g., JSON, SOAP, and/orlike parsers) that may be employed to parse (e.g., communications) data.Further, the parsing grammar may be used beyond message parsing, but mayalso be used to parse: databases, data collections, data stores,structured data, and/or the like. Again, the desired configuration willdepend upon the context, environment, and requirements of systemdeployment.

For example, in some implementations, the EXDB controller may beexecuting a PHP script implementing a Secure Sockets Layer (“SSL”)socket server via the information server, which listens to incomingcommunications on a server port to which a client may send data, e.g.,data encoded in JSON format. Upon identifying an incoming communication,the PHP script may read the incoming message from the client device,parse the received JSON-encoded text data to extract information fromthe JSON-encoded text data into PHP script variables, and store the data(e.g., client identifying information, etc.) and/or extractedinformation in a relational database accessible using the StructuredQuery Language (“SQL”). An exemplary listing, written substantially inthe form of PHP/SQL commands, to accept JSON-encoded input data from aclient device via a SSL connection, parse the data to extract variables,and store the data to a database, is provided below:

<?PHP header(′Content-Type: text/plain′); //set ip address and port tolisten to for incoming data $address = ‘192.168.0.100’; $port = 255;//create a server-side SSL socket, listen //for/accept incomingcommunication $sock = socket_create(AF_INET, SOCK_STREAM, 0);socket_bind($sock, $address, $port) or die(‘Could not bind to address’);socket_listen($sock); $client = socket_accept($sock); //read input datafrom client device in 1024 byte //blocks until end of message do {$input = “”; $input = socket_read($client, 1024); $data .= $input; }while($input != “”); // parse data to extract variables $obj =json_decode($data, true); // store input data in a databasemysql_connect(″10.1.1.1″,$srvr,$pass ); // access database servermysql_select(″CLIENT_DB.SQL″); // select database to appendmysql_query(“INSERT INTO UserTable (transmission) VALUES ($data)”); //add data to UserTable table in a CLIENT databasemysql_close(″CLIENT_DB.SQL″); // close connection to database ?>

Also, the following resources may be used to provide example embodimentsregarding SOAP parser implementation:

http://www.xav.com/perl/site/lib/SOAP/Parser.htmlhttp://publib.boulder.ibm.com/infocenter/tivihelp/v2r1/index.jsp?topic=/com.ibm.IBMDI.doc/referenceguide295.htm

and other parser implementations:

http://publib.boulder.ibm.com/infocenter/tivihelp/v2r1/index.jsp?topic=/com.ibm.IBMDI.doc/referenceguide259.htm

all of which are hereby expressly incorporated by reference.

Additional embodiments of the EXDB may include:

1. An encryptmatics extensible markup language data conversionprocessor-implemented system for increased efficiency in contextlessuser model sharing through the use of intermediary meta-languageprocessing, comprising:

-   -   means to receive an input model containing non-meta-data based        language commands;    -   means to retrieve input language definition records        corresponding to the input model language commands;    -   means to retrieve meta-data based language definition records;        and    -   means to generate a meta-data based language command file using        the input language definition records and the meta-based        language definition records.

2. The system of embodiment 1, additionally comprising:

-   -   means to determine at least one non-conditional logic flow block        in the input model language commands; and    -   means to generate a meta-data based language execution block for        the at least one non-conditional logic flow block.

3. The system of embodiment 1, additionally comprising:

-   -   means to determine a meta-data based language variable        initialization template; and    -   means to create a meta-data based language content block based        on the variable initialization template and non-variable        definitions contained within the input language model commands.

4. The system of embodiment 1, additionally comprising:

-   -   means to determine that the input model language commands        contain instructions to manipulate an external data source; and    -   means to determine that the external data source contains data        that may not be used by the input model.

5. The system of embodiment 4, additionally comprising:

-   -   means to execute iterative sequential anonymization commands on        the external data source;    -   means to determine that the external data source is available        for use by the input model; and    -   means to provide the anonymized external data source to the        input model commands for model execution on the anonymized data.

6. The system of embodiment 5, wherein determining that the externaldata source is available for use by the input model includes anindication that a minimum count of iterative sequential anonymizationcommands have been executed on the external data source.

7. The system of embodiment 1, additionally comprising:

-   -   means to provide the meta-data based language command file to a        user model sharing service if it is determined that the model        does not contain commands to manipulate an external data source        that contains data requiring anonymization.

8. The system of embodiment 1, wherein the input language definitionrecords contain executable logic commands.

9. The system of embodiment 8, wherein the executable logic commands arein a different language than the input model.

10. The system of embodiment 1, additionally comprising:

-   -   means to determine a mapping between the retrieved input        language definition record contents and the retrieved meta-data        based language definition record contents.

11. The system of embodiment 1, additionally comprising:

-   -   means to determine an offset spanning function required by an        input language operation.

12. The system of embodiment 11, wherein the offset spanning functionincludes a plurality of input language commands.

13. The system of embodiment 11, wherein determining an offset spanningfunction additionally comprises:

-   -   means to execute an upper bound input language operation test.

14. The system of embodiment 11, wherein determining an offset spanningfunction additionally comprises:

-   -   means to execute a lower bound input language operation test.

15. The system of embodiment 11, wherein determining an offset spanningfunction additionally comprises:

-   -   means to execute a user defined input language operation test.

16. The system of embodiment 1, wherein the meta-data based languagecommand file is extensible markup language.

17. The system of embodiment 1, wherein the meta-data based languagecommand file is JSON.

18. The system of embodiment 1, wherein the input model is created usinga non-compiled interpreted language.

Additional embodiments of the EXDB may include:

1. An encryptmatics extensible markup language data conversionprocessor-implemented apparatus for increased efficiency in contextlessuser model sharing through the use of intermediary meta-languageprocessing, comprising:

a memory;

a processor disposed in communication with said memory, and configuredto issue a plurality of processing instructions stored in the memory,wherein the processor issues instructions to:

-   -   receive an input model containing non-meta-data based language        commands;    -   retrieve input language definition records corresponding to the        input model language commands;    -   retrieve meta-data based language definition records; and    -   generate a meta-data based language command file using the input        language definition records and the meta-based language        definition records.

2. The apparatus of embodiment 1, additionally comprising instructionsto:

-   -   determine at least one non-conditional logic flow block in the        input model language commands; and    -   generate a meta-data based language execution block for the at        least one non-conditional logic flow block.

3. The apparatus of embodiment 1, additionally comprising instructionsto:

-   -   determine a meta-data based language variable initialization        template; and    -   create a meta-data based language content block based on the        variable initialization template and non-variable definitions        contained within the input language model commands.

4. The apparatus of embodiment 1, additionally comprising instructionsto:

-   -   determine that the input model language commands contain        instructions to manipulate an external data source; and    -   determine that the external data source contains data that may        not be used by the input model.

5. The apparatus of embodiment 4, additionally comprising instructionsto:

-   -   execute iterative sequential anonymization commands on the        external data source;    -   determine that the external data source is available for use by        the input model; and    -   provide the anonymized external data source to the input model        commands for model execution on the anonymized data.

6. The apparatus of embodiment 5, wherein determining that the externaldata source is available for use by the input model includes anindication that a minimum count of iterative sequential anonymizationcommands have been executed on the external data source.

7. The apparatus of embodiment 1, additionally comprising instructionsto:

-   -   provide the meta-data based language command file to a user        model sharing service if it is determined that the model does        not contain commands to manipulate an external data source that        contains data requiring anonymization.

8. The apparatus of embodiment 1, wherein the input language definitionrecords contain executable logic commands.

9. The apparatus of embodiment 8, wherein the executable logic commandsare in a different language than the input model.

10. The apparatus of embodiment 1, additionally comprising instructionsto:

-   -   determine a mapping between the retrieved input language        definition record contents and the retrieved meta-data based        language definition record contents.

11. The apparatus of embodiment 1, additionally comprising instructionsto:

-   -   determine an offset spanning function required by an input        language operation.

12. The apparatus of embodiment 11, wherein the offset spanning functionincludes a plurality of input language commands.

13. The apparatus of embodiment 11, wherein determining an offsetspanning function additionally comprises instructions to:

-   -   execute an upper bound input language operation test.

14. The apparatus of embodiment 11, wherein determining an offsetspanning function additionally comprises instructions to:

-   -   execute a lower bound input language operation test.

15. The apparatus of embodiment 11, wherein determining an offsetspanning function additionally comprises instructions to:

-   -   execute a user defined input language operation test.

16. The apparatus of embodiment 1, wherein the meta-data based languagecommand file is extensible markup language.

17. The apparatus of embodiment 1, wherein the meta-data based languagecommand file is JSON.

18. The apparatus of embodiment 1, wherein the input model is createdusing a non-compiled interpreted language.

Additional embodiments of the EXDB may include:

1. A non-transitory medium storing processor-issuable instructions forencryptmatics extensible markup language data conversion to:

-   -   receive an input model containing non-meta-data based language        commands;    -   retrieve input language definition records corresponding to the        input model language commands;    -   retrieve meta-data based language definition records; and    -   generate a meta-data based language command file using the input        language definition records and the meta-based language        definition records.

2. The medium of embodiment 1, additionally comprising instructions to:

-   -   determine at least one non-conditional logic flow block in the        input model language commands; and    -   generate a meta-data based language execution block for the at        least one non-conditional logic flow block.

3. The medium of embodiment 1, additionally comprising instructions to:

-   -   determine a meta-data based language variable initialization        template; and    -   create a meta-data based language content block based on the        variable initialization template and non-variable definitions        contained within the input language model commands.

4. The medium of embodiment 1, additionally comprising instructions to:

-   -   determine that the input model language commands contain        instructions to manipulate an external data source; and    -   determine that the external data source contains data that may        not be used by the input model.

5. The medium of embodiment 4, additionally comprising instructions to:

-   -   execute iterative sequential anonymization commands on the        external data source;    -   determine that the external data source is available for use by        the input model; and    -   provide the anonymized external data source to the input model        commands for model execution on the anonymized data.

6. The medium of embodiment 5, wherein determining that the externaldata source is available for use by the input model includes anindication that a minimum count of iterative sequential anonymizationcommands have been executed on the external data source.

7. The medium of embodiment 1, additionally comprising instructions to:

-   -   provide the meta-data based language command file to a user        model sharing service if it is determined that the model does        not contain commands to manipulate an external data source that        contains data requiring anonymization.

8. The medium of embodiment 1, wherein the input language definitionrecords contain executable logic commands.

9. The medium of embodiment 8, wherein the executable logic commands arein a different language than the input model.

10. The medium of embodiment 1, additionally comprising instructions to:

-   -   determine a mapping between the retrieved input language        definition record contents and the retrieved meta-data based        language definition record contents.

11. The medium of embodiment 1, additionally comprising instructions to:

-   -   determine an offset spanning function required by an input        language operation.

12. The medium of embodiment 11, wherein the offset spanning functionincludes a plurality of input language commands.

13. The medium of embodiment 11, wherein determining an offset spanningfunction additionally comprises instructions to:

-   -   execute an upper bound input language operation test.

14. The medium of embodiment 11, wherein determining an offset spanningfunction additionally comprises instructions to:

-   -   execute a lower bound input language operation test.

15. The medium of embodiment 11, wherein determining an offset spanningfunction additionally comprises instructions to:

-   -   execute a user defined input language operation test.

16. The medium of embodiment 1, wherein the meta-data based languagecommand file is extensible markup language.

17. The medium of embodiment 1, wherein the meta-data based languagecommand file is JSON.

18. The medium of embodiment 1, wherein the input model is created usinga non-compiled interpreted language.

Additional embodiments of the EXDB may include:

1. A centralized personal information platform processor-implementedmethod for enhancing transaction speed through the reduction of userinput data transfer requirements, comprising:

-   -   aggregating data records including search results, purchase        transaction data, service usage data, service enrollment data,        email data and social data;    -   identifying data field types within the data records and their        associated data values;    -   identifying an entity from the data field types and their        associated data values;    -   generating, via a processor, correlations of the entity to other        entities identifiable from the data field types and their        associated data values;    -   associating, via the processor, attributes to the entity by        drawing inferences related to the entity from the data field        types and their associated data values;    -   generating an updated profile and social graph of the entity        using the generated correlations and associated attributes; and    -   providing the updated profile and social graph for an automated        web form filling request.

2. The method of embodiment 1, further comprising:

-   -   generating a search query using the updated profile; and    -   providing the generated search query for further data        aggregation.

3. The method of embodiment 2, wherein the search query is a web searchquery.

4. The method of embodiment 2, wherein the search query is a socialsearch query.

5. The method of embodiment 2, wherein the search query is an email dataaggregation query.

6. The method of embodiment 4, wherein the updated profile includes asocial login credential; and wherein the social search query utilizesthe social login credential.

7. The method of embodiment 1, further comprising:

-   -   generating a search query using the updated social graph; and    -   providing the generated search query for further data        aggregation.

8. The method of embodiment 6, wherein the search query is a web searchquery.

9. The method of embodiment 6, wherein the search query is a socialsearch query.

10. The method of embodiment 8, wherein the updated profile includes asocial login credential; and wherein the social search query utilizesthe social login credential.

11. The method of embodiment 1, wherein the entity is one of: anInternet Protocol address; an individual; a pair of associatedindividuals; and a household; an office space; and an organization.

12. A merchant analytics platform processor-implemented method forreduced transaction wait processing requirements through the use ofcustomized transaction parameters based on a distributed linking nodemesh, comprising:

-   -   obtaining a request for a merchant analytics report including a        user identification;    -   aggregating user data of the user in a centralized personal        information database;    -   retrieving the aggregated user data in response to obtaining the        request for the merchant analytics report;    -   generating a user behavior profile using an analytical model,        based on the aggregated user data retrieved from the centralized        personal information database;    -   providing the user behavior profile as part of the merchant        analytics report.

13. The method of embodiment 12, further comprising:

-   -   retrieving aggregated user data for a plurality of users from        the centralized personal information database;    -   generating a statistical user behavior profile using an        analytical model, based on the aggregated user data for the        plurality of users retrieved from the centralized personal        information database; and    -   providing the statistical user behavior profile as part of the        merchant analytics report for the merchant.

14. The method of embodiment 12, wherein the retrieved aggregated userdata includes personally identifiable data associated with the useridentification.

15. The method of embodiment 14, further comprising:

-   -   anonymizing the retrieved aggregated user data by removing the        personally identifiable data from the retrieved aggregated user        data.

16. The method of embodiment 12, wherein the aggregated user dataincludes social data obtained from a social networking website.

17. The method of embodiment 16, wherein the user behavior profile isgenerated using the social data obtained from the social networkingwebsite.

18. The method of embodiment 18, wherein the social data includes usersocial posts to the social networking website.

19. The method of embodiment 12, further comprising:

-   -   determining a product or service having maximum likelihood of        being purchased by the user; and    -   providing an identification of the product or service as part of        the merchant analytics report.

20. The method of embodiment 13, wherein the statistical user behaviorprofile is generated using aggregated social data obtained from socialnetworking websites for the plurality of users, and retrieved from thecentralized personal information database.

21. The method of embodiment 12, further comprising:

-   -   triggering an investment action based on the merchant analytics        report.

22. An analytical model sharing processor-implemented method for privacyenhanced analytical model sharing through the use of contextual privacydataset modifications, comprising:

-   -   obtaining a request to publish an analytical model operating on        user data retrieved from a centralized personal information        database;    -   determining whether the analytical model includes a model        dataset;    -   upon determining that the analytical model includes a model        dataset, determining whether the model dataset includes        personally identifiable information; and    -   generating a determination of whether to accept the request to        publish the analytical model based on whether the model dataset        includes personally identifiable information.

23. The method of embodiment 22, further comprising:

-   -   determining that model dataset does not include personally        identifiable information;    -   generating a notification of acceptance of the request to        publish the analytical model;    -   providing the analytical model for publication; and    -   providing the notification of acceptance of the request to        publish the analytical model.

24. The method of embodiment 22, further comprising:

-   -   determining that model dataset includes personally identifiable        information;    -   upon determining that the model dataset includes personally        identifiable information, determining whether the analytical        model can be run in the absence of the personally identifiable        information; and    -   generating a determination of whether to accept the request to        publish the analytical model based on whether the analytical        model can be run in the absence of the personally identifiable        information.

25. The method of embodiment 24, further comprising:

-   -   determining that analytical model can be run in the absence of        the personally identifiable information;    -   extracting the personally identifiable information from the        model dataset;    -   providing the analytical model for publication excluding the        personally identifiable information from the model dataset; and    -   providing the notification of acceptance of the request to        publish the analytical model.

26. The method of embodiment 24, further comprising:

-   -   determining that the analytical model cannot be run in the        absence of the personally identifiable information;    -   upon determining that the analytical model cannot be run in the        absence of the personally identifiable information, determining        whether the analytical model can be run after anonymization of        the personally identifiable information; and    -   generating a determination of whether to accept the request to        publish the analytical model based on whether the analytical        model can be run after anonymization of the personally        identifiable information.

27. The method of embodiment 26, further comprising:

-   -   determining that the analytical model can be run after        anonymization of the personally identifiable information;    -   anonymizing the personally identifiable information in the model        dataset;    -   providing the analytical model for publication including the        anonymized personally identifiable information in the model        dataset; and    -   providing the notification of acceptance of the request to        publish the analytical model.

28. The method of embodiment 26, further comprising:

-   -   determining that the analytical model cannot be run after        anonymization of the personally identifiable information; and    -   providing a notification of rejection of the request to publish        the analytical model.

29. The method of embodiment 22, wherein the user data retrieved from acentralized personal information database is that of a single user.

30. The method of embodiment 22, wherein the user data retrieved from acentralized personal information database is aggregated user data.

31. The method of embodiment 22, wherein the analytical model ispublished to a publicly-accessible model sharing website.

32. An encryptmatics extensible markup language data conversionprocessor-implemented method for increased efficiency in contextlessuser model sharing through the use of intermediary meta-languageprocessing, comprising:

-   -   receiving an input model containing non-meta-data based language        commands;    -   retrieving input language definition records corresponding to        the input model language commands;    -   retrieving meta-data based language definition records; and    -   generating a meta-data based language command file using the        input language definition records and the meta-based language        definition records.

33. The method of embodiment 32, additionally comprising:

-   -   determining at least one non-conditional logic flow block in the        input model language commands; and    -   generating a meta-data based language execution block for the at        least one non-conditional logic flow block.

34. The method of embodiment 32, additionally comprising:

-   -   determining a meta-data based language variable initialization        template; and    -   creating a meta-data based language content block based on the        variable initialization template and non-variable definitions        contained within the input language model commands.

35. The method of embodiment 32, additionally comprising:

-   -   determining that the input model language commands contain        instructions to manipulate an external data source;    -   determining that the external data source contains data that may        not be used by the input model;    -   executing iterative sequential anonymization commands on the        external data source; determining that the external data source        is available for use by the input model; and    -   providing the anonymized external data source to the input model        commands for model execution on the anonymized data.

36. The method of embodiment 35, wherein determining that the externaldata source is available for use by the input model includes anindication that a minimum count of iterative sequential anonymizationcommands have been executed on the external data source.

37. The method of embodiment 32, additionally comprising:

-   -   providing the meta-data based language command file to a user        model sharing service if it is determined that the model does        not contain commands to manipulate an external data source that        contains data requiring anonymization.

38. An processor-implemented method, comprising:

-   -   aggregating, from a plurality of entities, raw mesh entries        comprising any of: emails, engagement transactions, financial        transactions, social media entries, into memory;    -   determining an mesh entry type for each raw mesh entry;    -   placing contents of each raw mesh entry into an unprocessed mesh        entry structure;    -   setting the mesh entry type for the unprocessed mesh entry from        the determined mesh entry type;    -   generating a dictionary hash entry from the raw mesh entry and        saving it into the unprocessed mesh entry structure;    -   updating a mesh entry dictionary with the unprocessed mesh entry        structure;    -   replicating the mesh entry dictionary to another location        without the raw mesh entry in the mesh entry structure, wherein        the replicated mesh entry dictionary is actionable for analysis        without the raw mesh entry and with the dictionary has entry and        set mesh entry type;    -   storing the unprocessed mesh entry structure into a        multi-directionally linked multimedia data mesh (MLMD mesh);    -   determining correlations within the unprocessed mesh entry        structure with other stored mesh entry structures in the MLMD        mesh;    -   creating links to the determined correlated stored mesh entry        structures and storing them in the stored unprocessed mesh entry        structure;    -   marking the unprocessed mesh entry structure as a processed mesh        entry structure.

39. The method of embodiment 38, wherein processed mesh entry structuresare updated with category, interest group, product type, price, andlocation information.

40. The method of embodiment 39, further, comprising:

-   -   obtaining a purchase request for a specified interest group, a        specified interest group qualifier, an unspecified merchant, an        unspecified product for a specified amount.

41. The method of embodiment 40, further, comprising:

-   -   wherein the unspecified product is determined by a consumer        specified interest group qualifier of the specified interest        group.

42. The method of embodiment 41, wherein the consumer specified interestgroup qualifier is any of best, most popular, most expensive, mostexclusive, best deal.

43. The method of embodiment 42, further, comprising:

-   -   quering the MLMD mesh with the purchase request for a specified        amount;    -   obtaining MLMD mesh query results for the purchase request;    -   querying merchants with the MLMD mesh query results for purchase        items satisfying the purchase request;    -   placing an order for purchase items satisfying the purchase        request.

44. The method of embodiment 43, further, comprising:

-   -   wherein if no purchase items satisfy the purchase request, the        purchase request is maintained until cancelled.

45. The method of embodiment 44, further, comprising:

-   -   wherein the maintained purchase request may result in a purchase        when merchant items satisfy the purchase request as such items        parameters change with time.

46. A centralized personal information platform processor-implementedsystem for enhancing transaction speed through the reduction of userinput data transfer requirements, comprising:

-   -   means to aggregate data records including search results,        purchase transaction data, service usage data, service        enrollment data, email data and social data;    -   means to identify data field types within the data records and        their associated data values;    -   means to identify an entity from the data field types and their        associated data values;    -   means to generate, via a processor, correlations of the entity        to other entities identifiable from the data field types and        their associated data values;    -   means to associate, via the processor, attributes to the entity        by drawing inferences related to the entity from the data field        types and their associated data values;    -   means to generate an updated profile and social graph of the        entity using the generated correlations and associated        attributes; and    -   means to provide the updated profile and social graph for an        automated web form filling request.

47. The system of embodiment 46, further comprising:

-   -   means to generate a search query using the updated profile; and    -   means to provide the generated search query for further data        aggregation.

48. The system of embodiment 47, wherein the search query is a websearch query.

49. The system of embodiment 47, wherein the search query is a socialsearch query.

50. The system of embodiment 47, wherein the search query is an emaildata aggregation query.

51. The system of embodiment 49, wherein the updated profile includes asocial login credential; and wherein the social search query utilizesthe social login credential.

52. The system of embodiment 46, further comprising:

-   -   means to generate a search query using the updated social graph;        and    -   means to provide the generated search query for further data        aggregation.

53. The system of embodiment 51, wherein the search query is a websearch query.

54. The system of embodiment 51, wherein the search query is a socialsearch query.

55. The system of embodiment 53, wherein the updated profile includes asocial login credential; and wherein the social search query utilizesthe social login credential.

56. The system of embodiment 46, wherein the entity is one of: anInternet Protocol address; an individual; a pair of associatedindividuals; and a household; an office space; and an organization.

57. A merchant analytics platform processor-implemented system forreduced transaction wait processing requirements through the use ofcustomized transaction parameters based on a distributed linking nodemesh, comprising:

-   -   means to obtain a request for a merchant analytics report        including a user identification;    -   means to aggregate user data of the user in a centralized        personal information database;    -   means to retrieve the aggregated user data in response to        obtaining the request for the merchant analytics report;    -   means to generate a user behavior profile using an analytical        model, based on the aggregated user data retrieved from the        centralized personal information database;    -   means to provide the user behavior profile as part of the        merchant analytics report.

58. The system of embodiment 57, further comprising:

-   -   means to retrieve aggregated user data for a plurality of users        from the centralized personal information database;    -   means to generate a statistical user behavior profile using an        analytical model, based on the aggregated user data for the        plurality of users retrieved from the centralized personal        information database; and    -   means to provide the statistical user behavior profile as part        of the merchant analytics report for the merchant.

59. The system of embodiment 57, wherein the retrieved aggregated userdata includes personally identifiable data associated with the useridentification.

60. The system of embodiment 59, further comprising:

-   -   means to anonymize the retrieved aggregated user data by        removing the personally identifiable data from the retrieved        aggregated user data.

61. The system of embodiment 57, wherein the aggregated user dataincludes social data obtained from a social networking website.

62. The system of embodiment 61, wherein the user behavior profile isgenerated using the social data obtained from the social networkingwebsite.

63. The system of embodiment 63, wherein the social data includes usersocial posts to the social networking website.

64. The system of embodiment 57, further comprising:

-   -   means to determine a product or service having maximum        likelihood of being purchased by the user; and    -   means to provide an identification of the product or service as        part of the merchant analytics report.

65. The system of embodiment 58, wherein the statistical user behaviorprofile is generated using aggregated social data obtained from socialnetworking websites for the plurality of users, and retrieved from thecentralized personal information database.

66. The system of embodiment 57, further comprising:

-   -   means to trigger an investment action based on the merchant        analytics report.

67. An analytical model sharing processor-implemented system for privacyenhanced analytical model sharing through the use of contextual privacydataset modifications, comprising:

-   -   means to obtain a request to publish an analytical model        operating on user data retrieved from a centralized personal        information database;    -   means to determine whether the analytical model includes a model        dataset;    -   means to upon determining that the analytical model includes a        model dataset, determining whether the model dataset includes        personally identifiable information; and    -   means to generate a determination of whether to accept the        request to publish the analytical model based on whether the        model dataset includes personally identifiable information.

68. The system of embodiment 67, further comprising:

-   -   means to determine that model dataset does not include        personally identifiable information;    -   means to generate a notification of acceptance of the request to        publish the analytical model;    -   means to provide the analytical model for publication; and    -   means to provide the notification of acceptance of the request        to publish the analytical model.

69. The system of embodiment 67, further comprising:

-   -   means to determine that model dataset includes personally        identifiable information;    -   means to upon determining that the model dataset includes        personally identifiable information, determining whether the        analytical model can be run in the absence of the personally        identifiable information; and    -   means to generate a determination of whether to accept the        request to publish the analytical model based on whether the        analytical model can be run in the absence of the personally        identifiable information.

70. The system of embodiment 69, further comprising:

-   -   means to determine that analytical model can be run in the        absence of the personally identifiable information;    -   means to extract the personally identifiable information from        the model dataset;    -   means to provide the analytical model for publication excluding        the personally identifiable information from the model dataset;        and    -   means to provide the notification of acceptance of the request        to publish the analytical model.

71. The system of embodiment 69, further comprising:

-   -   means to determine that the analytical model cannot be run in        the absence of the personally identifiable information;    -   means to upon determining that the analytical model cannot be        run in the absence of the personally identifiable information,        determining whether the analytical model can be run after        anonymization of the personally identifiable information; and    -   means to generate a determination of whether to accept the        request to publish the analytical model based on whether the        analytical model can be run after anonymization of the        personally identifiable information.

72. The system of embodiment 71, further comprising:

-   -   means to determine that the analytical model can be run after        anonymization of the personally identifiable information;    -   means to anonymize the personally identifiable information in        the model dataset;    -   means to provide the analytical model for publication including        the anonymized personally identifiable information in the model        dataset; and    -   means to provide the notification of acceptance of the request        to publish the analytical model.

73. The system of embodiment 71, further comprising:

-   -   means to determine that the analytical model cannot be run after        anonymization of the personally identifiable information; and    -   means to provide a notification of rejection of the request to        publish the analytical model.

74. The system of embodiment 67, wherein the user data retrieved from acentralized personal information database is that of a single user.

75. The system of embodiment 67, wherein the user data retrieved from acentralized personal information database is aggregated user data.

76. The system of embodiment 67, wherein the analytical model ispublished to a publicly-accessible model sharing website.

77. An encryptmatics extensible markup language data conversionprocessor-implemented system for increased efficiency in contextlessuser model sharing through the use of intermediary meta-languageprocessing, comprising:

-   -   means to receive an input model containing non-meta-data based        language commands;    -   means to retrieve input language definition records        corresponding to the input model language commands;    -   means to retrieve meta-data based language definition records;        and    -   means to generate a meta-data based language command file using        the input language definition records and the meta-based        language definition records.

78. The system of embodiment 77, additionally comprising:

-   -   means to determine at least one non-conditional logic flow block        in the input model language commands; and    -   means to generate a meta-data based language execution block for        the at least one non-conditional logic flow block.

79. The system of embodiment 77, additionally comprising:

-   -   means to determine a meta-data based language variable        initialization template; and    -   means to create a meta-data based language content block based        on the variable initialization template and non-variable        definitions contained within the input language model commands.

80. The system of embodiment 77, additionally comprising:

-   -   means to determine that the input model language commands        contain instructions to manipulate an external data source;    -   means to determine that the external data source contains data        that may not be used by the input model;    -   means to execute iterative sequential anonymization commands on        the external data source; determining that the external data        source is available for use by the input model; and    -   means to provide the anonymized external data source to the        input model commands for model execution on the anonymized data.

81. The system of embodiment 80, wherein determining that the externaldata source is available for use by the input model includes anindication that a minimum count of iterative sequential anonymizationcommands have been executed on the external data source.

82. The system of embodiment 77, additionally comprising:

-   -   means to provide the meta-data based language command file to a        user model sharing service if it is determined that the model        does not contain commands to manipulate an external data source        that contains data requiring anonymization.

83. An processor-implemented system, comprising:

means to aggregate, from a plurality of entities, raw mesh entriescomprising any of: emails, engagement transactions, financialtransactions, social media entries, into memory;

means to determine an mesh entry type for each raw mesh entry;

means to place contents of each raw mesh entry into an unprocessed meshentry structure;

means to set the mesh entry type for the unprocessed mesh entry from thedetermined mesh entry type;

means to generate a dictionary hash entry from the raw mesh entry andsaving it into the unprocessed mesh entry structure;

means to update a mesh entry dictionary with the unprocessed mesh entrystructure;

means to replicate the mesh entry dictionary to another location withoutthe raw mesh entry in the mesh entry structure, wherein the replicatedmesh entry dictionary is actionable for analysis without the raw meshentry and with the dictionary has entry and set mesh entry type;

means to store the unprocessed mesh entry structure into amulti-directionally linked multimedia data mesh (MLMD mesh);

means to determine correlations within the unprocessed mesh entrystructure with other stored mesh entry structures in the MLMD mesh;

means to create links to the determined correlated stored mesh entrystructures and storing them in the stored unprocessed mesh entrystructure;

means to mark the unprocessed mesh entry structure as a processed meshentry structure.

84. The system of embodiment 83, wherein processed mesh entry structuresare updated with category, interest group, product type, price, andlocation information.

85. The system of embodiment 84, further, comprising:

means to obtain a purchase request for a specified interest group, aspecified interest group qualifier, an unspecified merchant, anunspecified product for a specified amount.

86. The system of embodiment 85, further, comprising:

wherein the unspecified product is determined by a consumer specifiedinterest group qualifier of the specified interest group.

87. The system of embodiment 86, wherein the consumer specified interestgroup qualifier is any of best, most popular, most expensive, mostexclusive, best deal.

88. The system of embodiment 87, further, comprising:

means to query the MLMD mesh with the purchase request for a specifiedamount;

means to obtainin MLMD mesh query results for the purchase request;

means to query merchants with the MLMD mesh query results for purchaseitems satisfying the purchase request;

means to place an order for purchase items satisfying the purchaserequest.

89. The system of embodiment 88, further, comprising:

wherein if no purchase items satisfy the purchase request, the purchaserequest is maintained until cancelled.

90. The system of embodiment 89, further, comprising:

wherein the maintained purchase request may result in a purchase whenmerchant items satisfy the purchase request as such items parameterschange with time.

91. A centralized personal information platform processor-implementedapparatus for enhancing transaction speed through the reduction of userinput data transfer requirements, comprising:

a memory;

a processor disposed in communication with said memory, and configuredto issue a plurality of processing instructions stored in the memory,wherein the processor issues instructions to:

-   -   aggregate data records including search results, purchase        transaction data, service usage data, service enrollment data,        email data and social data;    -   identify data field types within the data records and their        associated data values;    -   identify an entity from the data field types and their        associated data values;    -   generate, via a processor, correlations of the entity to other        entities identifiable from the data field types and their        associated data values;    -   associate, via the processor, attributes to the entity by        drawing inferences related to the entity from the data field        types and their associated data values;    -   generate an updated profile and social graph of the entity using        the generated correlations and associated attributes; and    -   provide the updated profile and social graph for an automated        web form filling request.

92. The apparatus of embodiment 91, further comprising instructions to:

generate a search query using the updated profile; and

provide the generated search query for further data aggregation.

93. The apparatus of embodiment 92, wherein the search query is a websearch query.

94. The apparatus of embodiment 92, wherein the search query is a socialsearch query.

95. The apparatus of embodiment 92, wherein the search query is an emaildata aggregation query.

96. The apparatus of embodiment 94, wherein the updated profile includesa social login credential; and wherein the social search query utilizesthe social login credential.

97. The apparatus of embodiment 91, further comprising instructions to:

-   -   generate a search query using the updated social graph; and    -   provide the generated search query for further data aggregation.

98. The apparatus of embodiment 96, wherein the search query is a websearch query.

99. The apparatus of embodiment 96, wherein the search query is a socialsearch query.

100. The apparatus of embodiment 98, wherein the updated profileincludes a social login credential; and wherein the social search queryutilizes the social login credential.

101. The apparatus of embodiment 91, wherein the entity is one of: anInternet Protocol address; an individual; a pair of associatedindividuals; and a household; an office space; and an organization.

102. A merchant analytics platform processor-implemented apparatus forreduced transaction wait processing requirements through the use ofcustomized transaction parameters based on a distributed linking nodemesh, comprising:

a memory;

a processor disposed in communication with said memory, and configuredto issue a plurality of processing instructions stored in the memory,wherein the processor issues instructions to:

-   -   obtainin a request for a merchant analytics report including a        user identification;    -   aggregate user data of the user in a centralized personal        information database;    -   retrieve the aggregated user data in response to obtaining the        request for the merchant analytics report;    -   generate a user behavior profile using an analytical model,        based on the aggregated user data retrieved from the centralized        personal information database;    -   provide the user behavior profile as part of the merchant        analytics report.

103. The apparatus of embodiment 102, further comprising instructionsto:

-   -   retrieve aggregated user data for a plurality of users from the        centralized personal information database;    -   generate a statistical user behavior profile using an analytical        model, based on the aggregated user data for the plurality of        users retrieved from the centralized personal information        database; and    -   provide the statistical user behavior profile as part of the        merchant analytics report for the merchant.

104. The apparatus of embodiment 102, wherein the retrieved aggregateduser data includes personally identifiable data associated with the useridentification.

105. The apparatus of embodiment 104, further comprising instructionsto:

-   -   anonymize the retrieved aggregated user data by removing the        personally identifiable data from the retrieved aggregated user        data.

106. The apparatus of embodiment 102, wherein the aggregated user dataincludes social data obtained from a social networking website.

107. The apparatus of embodiment 106, wherein the user behavior profileis generated using the social data obtained from the social networkingwebsite.

108. The apparatus of embodiment 108, wherein the social data includesuser social posts to the social networking website.

109. The apparatus of embodiment 102, further comprising instructionsto:

-   -   determine a product or service having maximum likelihood of        being purchased by the user; and    -   provide an identification of the product or service as part of        the merchant analytics report.

110. The apparatus of embodiment 103, wherein the statistical userbehavior profile is generated using aggregated social data obtained fromsocial networking websites for the plurality of users, and retrievedfrom the centralized personal information database.

111. The apparatus of embodiment 102, further comprising instructionsto:

-   -   trigger an investment action based on the merchant analytics        report.

112. An analytical model sharing processor-implemented apparatus forprivacy enhanced analytical model sharing through the use of contextualprivacy dataset modifications, comprising:

a memory;

a processor disposed in communication with said memory, and configuredto issue a plurality of processing instructions stored in the memory,wherein the processor issues instructions to:

-   -   obtain a request to publish an analytical model operating on        user data retrieved from a centralized personal information        database;    -   determine whether the analytical model includes a model dataset;    -   upon determining that the analytical model includes a model        dataset, determining whether the model dataset includes        personally identifiable information; and    -   generate a determination of whether to accept the request to        publish the analytical model based on whether the model dataset        includes personally identifiable information.

113. The apparatus of embodiment 112, further comprising instructionsto:

-   -   determine that model dataset does not include personally        identifiable information;    -   generate a notification of acceptance of the request to publish        the analytical model;    -   provide the analytical model for publication; and    -   provide the notification of acceptance of the request to publish        the analytical model.

114. The apparatus of embodiment 112, further comprising instructionsto:

-   -   determine that model dataset includes personally identifiable        information;    -   upon determining that the model dataset includes personally        identifiable information, determining whether the analytical        model can be run in the absence of the personally identifiable        information; and    -   generate a determination of whether to accept the request to        publish the analytical model based on whether the analytical        model can be run in the absence of the personally identifiable        information.

115. The apparatus of embodiment 114, further comprising instructionsto:

-   -   determine that analytical model can be run in the absence of the        personally identifiable information;    -   extract the personally identifiable information from the model        dataset;    -   provide the analytical model for publication excluding the        personally identifiable information from the model dataset; and    -   provide the notification of acceptance of the request to publish        the analytical model.

116. The apparatus of embodiment 114, further comprising instructionsto:

-   -   determine that the analytical model cannot be run in the absence        of the personally identifiable information;    -   upon determining that the analytical model cannot be run in the        absence of the personally identifiable information, determining        whether the analytical model can be run after anonymization of        the personally identifiable information; and    -   generate a determination of whether to accept the request to        publish the analytical model based on whether the analytical        model can be run after anonymization of the personally        identifiable information.

117. The apparatus of embodiment 116, further comprising instructionsto:

-   -   determine that the analytical model can be run after        anonymization of the personally identifiable information;    -   anonymize the personally identifiable information in the model        dataset;    -   provide the analytical model for publication including the        anonymized personally identifiable information in the model        dataset; and    -   provide the notification of acceptance of the request to publish        the analytical model.

118. The apparatus of embodiment 116, further comprising instructionsto:

-   -   determine that the analytical model cannot be run after        anonymization of the personally identifiable information; and    -   provide a notification of rejection of the request to publish        the analytical model.

119. The apparatus of embodiment 112, wherein the user data retrievedfrom a centralized personal information database is that of a singleuser.

120. The apparatus of embodiment 112, wherein the user data retrievedfrom a centralized personal information database is aggregated userdata.

121. The apparatus of embodiment 112, wherein the analytical model ispublished to a publicly-accessible model sharing website.

122. An encryptmatics extensible markup language data conversionprocessor-implemented apparatus for increased efficiency in contextlessuser model sharing through the use of intermediary meta-languageprocessing, comprising:

a memory;

a processor disposed in communication with said memory, and configuredto issue a plurality of processing instructions stored in the memory,wherein the processor issues instructions to:

-   -   receive an input model containing non-meta-data based language        commands;    -   retrieve input language definition records corresponding to the        input model language commands;    -   retrieve meta-data based language definition records; and    -   generate a meta-data based language command file using the input        language definition records and the meta-based language        definition records.

123. The apparatus of embodiment 122, additionally comprisinginstructions to:

-   -   determine at least one non-conditional logic flow block in the        input model language commands; and    -   generate a meta-data based language execution block for the at        least one non-conditional logic flow block.

124. The apparatus of embodiment 122, additionally comprisinginstructions to:

-   -   determine a meta-data based language variable initialization        template; and    -   create a meta-data based language content block based on the        variable initialization template and non-variable definitions        contained within the input language model commands.

125. The apparatus of embodiment 122, additionally comprisinginstructions to:

-   -   determine that the input model language commands contain        instructions to manipulate an external data source;    -   determine that the external data source contains data that may        not be used by the input model;    -   execute iterative sequential anonymization commands on the        external data source; determining that the external data source        is available for use by the input model; and    -   provide the anonymized external data source to the input model        commands for model execution on the anonymized data.

126. The apparatus of embodiment 125, wherein determining that theexternal data source is available for use by the input model includes anindication that a minimum count of iterative sequential anonymizationcommands have been executed on the external data source.

127. The apparatus of embodiment 122, additionally comprisinginstructions to:

-   -   provide the meta-data based language command file to a user        model sharing service if it is determined that the model does        not contain commands to manipulate an external data source that        contains data requiring anonymization.

128. An processor-implemented apparatus, comprising:

a memory;

a processor disposed in communication with said memory, and configuredto issue a plurality of processing instructions stored in the memory,wherein the processor issues instructions to:

-   -   aggregate, from a plurality of entities, raw mesh entries        comprising any of: emails, engagement transactions, financial        transactions, social media entries, into memory;    -   determine an mesh entry type for each raw mesh entry;    -   place contents of each raw mesh entry into an unprocessed mesh        entry structure;    -   set the mesh entry type for the unprocessed mesh entry from the        determined mesh entry type;    -   generate a dictionary hash entry from the raw mesh entry and        saving it into the unprocessed mesh entry structure;    -   update a mesh entry dictionary with the unprocessed mesh entry        structure;    -   replicate the mesh entry dictionary to another location without        the raw mesh entry in the mesh entry structure, wherein the        replicated mesh entry dictionary is actionable for analysis        without the raw mesh entry and with the dictionary has entry and        set mesh entry type;    -   store the unprocessed mesh entry structure into a        multi-directionally linked multimedia data mesh (MLMD mesh);    -   determine correlations within the unprocessed mesh entry        structure with other stored mesh entry structures in the MLMD        mesh;    -   create links to the determined correlated stored mesh entry        structures and storing them in the stored unprocessed mesh entry        structure;    -   mark the unprocessed mesh entry structure as a processed mesh        entry structure.

129. The apparatus of embodiment 128, wherein processed mesh entrystructures are updated with category, interest group, product type,price, and location information.

130. The apparatus of embodiment 129, further, comprising instructionsto:

obtain a purchase request for a specified interest group, a specifiedinterest group qualifier, an unspecified merchant, an unspecifiedproduct for a specified amount.

131. The apparatus of embodiment 130, further, comprising instructionsto:

wherein the unspecified product is determined by a consumer specifiedinterest group qualifier of the specified interest group.

132. The apparatus of embodiment 131, wherein the consumer specifiedinterest group qualifier is any of best, most popular, most expensive,most exclusive, best deal.

133. The apparatus of embodiment 132, further, comprising instructionsto:

query the MLMD mesh with the purchase request for a specified amount;

obtain MLMD mesh query results for the purchase request;

query merchants with the MLMD mesh query results for purchase itemssatisfying the purchase request;

place an order for purchase items satisfying the purchase request.

134. The apparatus of embodiment 133, further, comprising instructionsto:

wherein if no purchase items satisfy the purchase request, the purchaserequest is maintained until cancelled.

135. The apparatus of embodiment 134, further, comprising instructionsto:

wherein the maintained purchase request may result in a purchase whenmerchant items satisfy the purchase request as such items parameterschange with time.

136. A non-transitory medium storing processor-issuable instructions fora centralized personal information platform processor-implemented to:

-   -   aggregate data records including search results, purchase        transaction data, service usage data, service enrollment data,        email data and social data;    -   identify data field types within the data records and their        associated data values;    -   identify an entity from the data field types and their        associated data values;    -   generate, via a processor, correlations of the entity to other        entities identifiable from the data field types and their        associated data values;    -   associate, via the processor, attributes to the entity by        drawing inferences related to the entity from the data field        types and their associated data values;    -   generate an updated profile and social graph of the entity using        the generated correlations and associated attributes; and    -   provide the updated profile and social graph for an automated        web form filling request.

137. The medium of embodiment 136, further comprising instructions to:

generate a search query using the updated profile; and

provide the generated search query for further data aggregation.

138. The medium of embodiment 137, wherein the search query is a websearch query.

139. The medium of embodiment 137, wherein the search query is a socialsearch query.

140. The medium of embodiment 137, wherein the search query is an emaildata aggregation query.

141. The medium of embodiment 139, wherein the updated profile includesa social login credential; and wherein the social search query utilizesthe social login credential.

142. The medium of embodiment 136, further comprising instructions to:

-   -   generate a search query using the updated social graph; and    -   provide the generated search query for further data aggregation.

143. The medium of embodiment 141, wherein the search query is a websearch query.

144. The medium of embodiment 141, wherein the search query is a socialsearch query.

145. The medium of embodiment 143, wherein the updated profile includesa social login credential; and wherein the social search query utilizesthe social login credential.

146. The medium of embodiment 136, wherein the entity is one of: anInternet Protocol address; an individual; a pair of associatedindividuals; and a household; an office space; and an organization.

147. A merchant analytics platform processor-implemented medium storinginstructions for reduced transaction wait processing requirementsthrough the use of customized transaction parameters based on adistributed linking node mesh to:

-   -   obtain a request for a merchant analytics report including a        user identification;    -   aggregate user data of the user in a centralized personal        information database;    -   retrieve the aggregated user data in response to obtaining the        request for the merchant analytics report;    -   generate a user behavior profile using an analytical model,        based on the aggregated user data retrieved from the centralized        personal information database;    -   provide the user behavior profile as part of the merchant        analytics report.

148. The medium of embodiment 147, further comprising instructions to:

-   -   retrieve aggregated user data for a plurality of users from the        centralized personal information database;    -   generate a statistical user behavior profile using an analytical        model, based on the aggregated user data for the plurality of        users retrieved from the centralized personal information        database; and    -   provide the statistical user behavior profile as part of the        merchant analytics report for the merchant.

149. The medium of embodiment 147, wherein the retrieved aggregated userdata includes personally identifiable data associated with the useridentification.

150. The medium of embodiment 149, further comprising instructions to:

-   -   anonymize the retrieved aggregated user data by removing the        personally identifiable data from the retrieved aggregated user        data.

151. The medium of embodiment 147, wherein the aggregated user dataincludes social data obtained from a social networking website.

152. The medium of embodiment 151, wherein the user behavior profile isgenerated using the social data obtained from the social networkingwebsite.

153. The medium of embodiment 153, wherein the social data includes usersocial posts to the social networking website.

154. The medium of embodiment 147, further comprising instructions to:

-   -   determine a product or service having maximum likelihood of        being purchased by the user; and    -   provide an identification of the product or service as part of        the merchant analytics report.

155. The medium of embodiment 148, wherein the statistical user behaviorprofile is generated using aggregated social data obtained from socialnetworking websites for the plurality of users, and retrieved from thecentralized personal information database.

156. The medium of embodiment 147, further comprising instructions to:

-   -   trigger an investment action based on the merchant analytics        report.

157. An analytical model sharing processor-implemented medium forprivacy enhanced analytical model sharing through the use of contextualprivacy dataset modifications, comprising instructions to:

-   -   obtain a request to publish an analytical model operating on        user data retrieved from a centralized personal information        database;    -   determine whether the analytical model includes a model dataset;    -   upon determining that the analytical model includes a model        dataset, determining whether the model dataset includes        personally identifiable information; and    -   generate a determination of whether to accept the request to        publish the analytical model based on whether the model dataset        includes personally identifiable information.

158. The medium of embodiment 157, further comprising instructions to:

-   -   determine that model dataset does not include personally        identifiable information;    -   generate a notification of acceptance of the request to publish        the analytical model;    -   provide the analytical model for publication; and    -   provide the notification of acceptance of the request to publish        the analytical model.

159. The medium of embodiment 157, further comprising instructions to:

-   -   determine that model dataset includes personally identifiable        information;    -   upon determining that the model dataset includes personally        identifiable information, determining whether the analytical        model can be run in the absence of the personally identifiable        information; and    -   generate a determination of whether to accept the request to        publish the analytical model based on whether the analytical        model can be run in the absence of the personally identifiable        information.

160. The medium of embodiment 159, further comprising instructions to:

-   -   determine that analytical model can be run in the absence of the        personally identifiable information;    -   extract the personally identifiable information from the model        dataset;    -   provide the analytical model for publication excluding the        personally identifiable information from the model dataset; and    -   provide the notification of acceptance of the request to publish        the analytical model.

161. The medium of embodiment 159, further comprising instructions to:

-   -   determine that the analytical model cannot be run in the absence        of the personally identifiable information;    -   upon determining that the analytical model cannot be run in the        absence of the personally identifiable information, determining        whether the analytical model can be run after anonymization of        the personally identifiable information; and    -   generate a determination of whether to accept the request to        publish the analytical model based on whether the analytical        model can be run after anonymization of the personally        identifiable information.

162. The medium of embodiment 161, further comprising instructions to:

-   -   determine that the analytical model can be run after        anonymization of the personally identifiable information;    -   anonymize the personally identifiable information in the model        dataset;    -   provide the analytical model for publication including the        anonymized personally identifiable information in the model        dataset; and    -   provide the notification of acceptance of the request to publish        the analytical model.

163. The medium of embodiment 161, further comprising instructions to:

-   -   determine that the analytical model cannot be run after        anonymization of the personally identifiable information; and    -   provide a notification of rejection of the request to publish        the analytical model.

164. The medium of embodiment 157, wherein the user data retrieved froma centralized personal information database is that of a single user.

165. The medium of embodiment 157, wherein the user data retrieved froma centralized personal information database is aggregated user data.

166. The medium of embodiment 157, wherein the analytical model ispublished to a publicly-accessible model sharing website.

167. An encryptmatics extensible markup language data conversionprocessor-implemented medium storing instructions for increasedefficiency in contextless user model sharing through the use ofintermediary meta-language processing to:

-   -   receive an input model containing non-meta-data based language        commands;    -   retrieve input language definition records corresponding to the        input model language commands;    -   retrieve meta-data based language definition records; and    -   generate a meta-data based language command file using the input        language definition records and the meta-based language        definition records.

168. The medium of embodiment 167, additionally comprising instructionsto:

-   -   determine at least one non-conditional logic flow block in the        input model language commands; and    -   generate a meta-data based language execution block for the at        least one non-conditional logic flow block.

169. The medium of embodiment 167, additionally comprising instructionsto:

-   -   determine a meta-data based language variable initialization        template; and    -   create a meta-data based language content block based on the        variable initialization template and non-variable definitions        contained within the input language model commands.

170. The medium of embodiment 167, additionally comprising instructionsto:

-   -   determine that the input model language commands contain        instructions to manipulate an external data source;    -   determine that the external data source contains data that may        not be used by the input model;    -   execute iterative sequential anonymization commands on the        external data source; determine that the external data source is        available for use by the input model; and    -   provide the anonymized external data source to the input model        commands for model execution on the anonymized data.

171. The medium of embodiment 170, wherein determining that the externaldata source is available for use by the input model includes anindication that a minimum count of iterative sequential anonymizationcommands have been executed on the external data source.

172. The medium of embodiment 167, additionally comprising instructionsto:

-   -   provide the meta-data based language command file to a user        model sharing service if it is determined that the model does        not contain commands to manipulate an external data source that        contains data requiring anonymization.

173. An processor-implemented medium containing instructions to:

aggregate, from a plurality of entities, raw mesh entries comprising anyof: emails, engagement transactions, financial transactions, socialmedia entries, into memory;

determine an mesh entry type for each raw mesh entry;

place contents of each raw mesh entry into an unprocessed mesh entrystructure;

set the mesh entry type for the unprocessed mesh entry from thedetermined mesh entry type;

generate a dictionary hash entry from the raw mesh entry and saving itinto the unprocessed mesh entry structure;

update a mesh entry dictionary with the unprocessed mesh entrystructure;

replicate the mesh entry dictionary to another location without the rawmesh entry in the mesh entry structure, wherein the replicated meshentry dictionary is actionable for analysis without the raw mesh entryand with the dictionary has entry and set mesh entry type;

store the unprocessed mesh entry structure into a multi-directionallylinked multimedia data mesh (MLMD mesh);

determine correlations within the unprocessed mesh entry structure withother stored mesh entry structures in the MLMD mesh;

create links to the determined correlated stored mesh entry structuresand storing them in the stored unprocessed mesh entry structure;

mark the unprocessed mesh entry structure as a processed mesh entrystructure.

174. The medium of embodiment 173, wherein processed mesh entrystructures are updated with category, interest group, product type,price, and location information.

175. The medium of embodiment 174, further, comprising instructions to:

-   -   obtain a purchase request for a specified interest group, a        specified interest group qualifier, an unspecified merchant, an        unspecified product for a specified amount.

176. The medium of embodiment 175, further, comprising instructions to:

-   -   wherein the unspecified product is determined by a consumer        specified interest group qualifier of the specified interest        group.

177. The medium of embodiment 176, wherein the consumer specifiedinterest group qualifier is any of best, most popular, most expensive,most exclusive, best deal.

178. The medium of embodiment 177, further, comprising instructions to:

-   -   query the MLMD mesh with the purchase request for a specified        amount;    -   obtain MLMD mesh query results for the purchase request;    -   query merchants with the MLMD mesh query results for purchase        items satisfying the purchase request;    -   place an order for purchase items satisfying the purchase        request.

179. The medium of embodiment 178, further, comprising instructions to:

wherein if no purchase items satisfy the purchase request, the purchaserequest is maintained until cancelled.

180. The medium of embodiment 179, further, comprising instructions to:

-   -   wherein the maintained purchase request may result in a purchase        when merchant items satisfy the purchase request as such items        parameters change with time.

In order to address various issues and advance the art, the entirety ofthis application for EXDB (including the Cover Page, Title, Headings,Field, Background, Summary, Brief Description of the Drawings, DetailedDescription, Claims, Abstract, Figures, Appendices, and otherwise)shows, by way of illustration, various embodiments in which the claimedinnovations may be practiced. The advantages and features of theapplication are of a representative sample of embodiments only, and arenot exhaustive and/or exclusive. They are presented only to assist inunderstanding and teach the claimed principles. It should be understoodthat they are not representative of all claimed innovations. As such,certain aspects of the disclosure have not been discussed herein. Thatalternate embodiments may not have been presented for a specific portionof the innovations or that further undescribed alternate embodiments maybe available for a portion is not to be considered a disclaimer of thosealternate embodiments. It will be appreciated that many of thoseundescribed embodiments incorporate the same principles of theinnovations and others are equivalent. Thus, it is to be understood thatother embodiments may be utilized and functional, logical, operational,organizational, structural and/or topological modifications may be madewithout departing from the scope and/or spirit of the disclosure. Assuch, all examples and/or embodiments are deemed to be non-limitingthroughout this disclosure. Also, no inference should be drawn regardingthose embodiments discussed herein relative to those not discussedherein other than it is as such for purposes of reducing space andrepetition. For instance, it is to be understood that the logical and/ortopological structure of any combination of any program components (acomponent collection), other components and/or any present feature setsas described in the figures and/or throughout are not limited to a fixedoperating order and/or arrangement, but rather, any disclosed order isexemplary and all equivalents, regardless of order, are contemplated bythe disclosure. Furthermore, it is to be understood that such featuresare not limited to serial execution, but rather, any number of threads,processes, services, servers, and/or the like that may executeasynchronously, concurrently, in parallel, simultaneously,synchronously, and/or the like are contemplated by the disclosure. Assuch, some of these features may be mutually contradictory, in that theycannot be simultaneously present in a single embodiment. Similarly, somefeatures are applicable to one aspect of the innovations, andinapplicable to others. In addition, the disclosure includes otherinnovations not presently claimed. Applicant reserves all rights inthose presently unclaimed innovations including the right to claim suchinnovations, file additional applications, continuations, continuationsin part, divisions, and/or the like thereof. As such, it should beunderstood that advantages, embodiments, examples, functional, features,logical, operational, organizational, structural, topological, and/orother aspects of the disclosure are not to be considered limitations onthe disclosure as defined by the claims or limitations on equivalents tothe claims. It is to be understood that, depending on the particularneeds and/or characteristics of a EXDB individual and/or enterpriseuser, database configuration and/or relational model, data type, datatransmission and/or network framework, syntax structure, and/or thelike, various embodiments of the EXDB, may be implemented that enable agreat deal of flexibility and customization. For example, aspects of theEXDB may be adapted for restaurant dining, online shopping,brick-and-mortar shopping, secured information processing, and/or thelike. While various embodiments and discussions of the EXDB have beendirected to electronic purchase transactions, however, it is to beunderstood that the embodiments described herein may be readilyconfigured and/or customized for a wide variety of other applicationsand/or implementations.

What is claimed is:
 1. An encryptmatics extensible markup language dataconversion processor-implemented method for increased efficiency incontextless user model sharing through the use of intermediarymeta-language processing, comprising: receiving an input modelcontaining non-meta-data based language commands; retrieving inputlanguage definition records corresponding to the input model languagecommands; retrieving meta-data based language definition records; andgenerating a meta-data based language command file using the inputlanguage definition records and the meta-based language definitionrecords.
 2. The method of claim 1, additionally comprising: determiningat least one non-conditional logic flow block in the input modellanguage commands; and generating a meta-data based language executionblock for the at least one non-conditional logic flow block.
 3. Themethod of claim 1, additionally comprising: determining a meta-databased language variable initialization template; and creating ameta-data based language content block based on the variableinitialization template and non-variable definitions contained withinthe input language model commands.
 4. The method of claim 1,additionally comprising: determining that the input model languagecommands contain instructions to manipulate an external data source; anddetermining that the external data source contains data that may not beused by the input model.
 5. The method of claim 4, additionallycomprising: executing iterative sequential anonymization commands on theexternal data source; determining that the external data source isavailable for use by the input model; and providing the anonymizedexternal data source to the input model commands for model execution onthe anonymized data.
 6. The method of claim 5, wherein determining thatthe external data source is available for use by the input modelincludes an indication that a minimum count of iterative sequentialanonymization commands have been executed on the external data source.7. The method of claim 1, additionally comprising: providing themeta-data based language command file to a user model sharing service ifit is determined that the model does not contain commands to manipulatean external data source that contains data requiring anonymization. 8.The method of claim 1, wherein the input language definition recordscontain executable logic commands.
 9. The method of claim 8, wherein theexecutable logic commands are in a different language than the inputmodel.
 10. The method of claim 1, additionally comprising: determining amapping between the retrieved input language definition record contentsand the retrieved meta-data based language definition record contents.11. The method of claim 1, additionally comprising: determining anoffset spanning function required by an input language operation. 12.The method of claim 11, wherein the offset spanning function includes aplurality of input language commands.
 13. The method of claim 11,wherein determining an offset spanning function additionally comprises:executing an upper bound input language operation test.
 14. The methodof claim 11, wherein determining an offset spanning functionadditionally comprises: executing a lower bound input language operationtest.
 15. The method of claim 11, wherein determining an offset spanningfunction additionally comprises: executing a user defined input languageoperation test.
 16. The method of claim 1, wherein the meta-data basedlanguage command file is extensible markup language.
 17. The method ofclaim 1, wherein the meta-data based language command file is JSON. 18.The method of claim 1, wherein the input model is created using anon-compiled interpreted language.
 19. An encryptmatics extensiblemarkup language data conversion processor-implemented method forincreased efficiency in contextless user model sharing through the useof intermediary meta-language processing, comprising: receiving an inputmodel containing non-meta-data based language commands; retrieving inputlanguage definition records corresponding to the input model languagecommands; retrieving meta-data based language definition records;determining at least one non-conditional logic flow block in the inputmodel language commands; generating a meta-data based language executionblock for the at least one non-conditional logic flow block; determininga meta-data based language variable initialization template; creating ameta-data based language content block based on the variableinitialization template and non-variable definitions contained withinthe input language model commands; determining that the input modellanguage commands contain instructions to manipulate an external datasource; determining that the external data source contains data that maynot be used by the input model; executing iterative sequentialanonymization commands on the external data source; determining that theexternal data source is available for use by the input model; providingthe anonymized external data source to the input model commands formodel execution on the anonymized data; and generating a meta-data basedlanguage command file using the input language definition records andthe meta-based language definition records.
 20. An encryptmaticsextensible markup language data conversion processor-implemented methodfor increased efficiency in contextless user model sharing through theuse of intermediary meta-language processing, comprising: aggregating,from a plurality of entities, raw mesh entries comprising any of:emails, engagement transactions, financial transactions, social mediaentries, into memory; determining an mesh entry type for each raw meshentry; placing contents of each raw mesh entry into an unprocessed meshentry structure; setting the mesh entry type for the unprocessed meshentry from the determined mesh entry type; generating a dictionary hashentry from the raw mesh entry and saving it into the unprocessed meshentry structure; updating a mesh entry dictionary with the unprocessedmesh entry structure; replicating the mesh entry dictionary to anotherlocation without the raw mesh entry in the mesh entry structure, whereinthe replicated mesh entry dictionary is actionable for analysis withoutthe raw mesh entry and with the dictionary has entry and set mesh entrytype; storing the unprocessed mesh entry structure into amulti-directionally linked multimedia data mesh (MLMD mesh); determiningcorrelations within the unprocessed mesh entry structure with otherstored mesh entry structures in the MLMD mesh; creating links to thedetermined correlated stored mesh entry structures and storing them inthe stored unprocessed mesh entry structure; and marking the unprocessedmesh entry structure as a processed mesh entry structure.